diff --git a/Notebooks/Spark-Example-21-Mllib-Regression.ipynb b/Notebooks/Spark-Example-21-Mllib-Regression.ipynb
index f082dd4..395d3d7 100644
--- a/Notebooks/Spark-Example-21-Mllib-Regression.ipynb
+++ b/Notebooks/Spark-Example-21-Mllib-Regression.ipynb
@@ -194,8 +194,8 @@
        "</div>"
       ],
       "text/plain": [
-       "   Country_Code         Country  Total_Library_Size  Num_TV_Shows  Num_Movies   \n",
-       "0            ar       Argentina                4760          3154        1606  \\\n",
+       "   Country_Code         Country  Total_Library_Size  Num_TV_Shows  Num_Movies  \\\n",
+       "0            ar       Argentina                4760          3154        1606   \n",
        "1            au       Australia                6114          4050        2064   \n",
        "2            at         Austria                5640          3779        1861   \n",
        "3            be         Belgium                4990          3374        1616   \n",
@@ -347,15 +347,15 @@
        "</div>"
       ],
       "text/plain": [
-       "  Country_Code    Country  Total_Library_Size  Num_TV_Shows  Num_Movies   \n",
-       "0           ar  Argentina                4760          3154        1606  \\\n",
+       "  Country_Code    Country  Total_Library_Size  Num_TV_Shows  Num_Movies  \\\n",
+       "0           ar  Argentina                4760          3154        1606   \n",
        "1           au  Australia                6114          4050        2064   \n",
        "2           at    Austria                5640          3779        1861   \n",
        "3           be    Belgium                4990          3374        1616   \n",
        "4           bo    Bolivia                4991          3155        1836   \n",
        "\n",
-       "   Cost_Per_Month_Basic  Cost_Per_Month_Standard  Cost_Per_Month_Premium   \n",
-       "0                  3.74                     6.30                    9.26  \\\n",
+       "   Cost_Per_Month_Basic  Cost_Per_Month_Standard  Cost_Per_Month_Premium  \\\n",
+       "0                  3.74                     6.30                    9.26   \n",
        "1                  7.84                    12.12                   16.39   \n",
        "2                  9.03                    14.67                   20.32   \n",
        "3                 10.16                    15.24                   20.32   \n",
@@ -405,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -520,15 +520,15 @@
        "</div>"
       ],
       "text/plain": [
-       "  Country_Code      Country  Total_Library_Size  Num_TV_Shows  Num_Movies   \n",
-       "0           at      Austria                5640          3779        1861  \\\n",
+       "  Country_Code      Country  Total_Library_Size  Num_TV_Shows  Num_Movies  \\\n",
+       "0           at      Austria                5640          3779        1861   \n",
        "1           be      Belgium                4990          3374        1616   \n",
        "2           br       Brazil                4972          3162        1810   \n",
        "3           ca       Canada                6239          4311        1928   \n",
        "4           ch  Switzerland                5506          3654        1852   \n",
        "\n",
-       "   Cost_Per_Month_Basic  Cost_Per_Month_Standard  Cost_Per_Month_Premium   \n",
-       "0                  9.03                    14.67                   20.32  \\\n",
+       "   Cost_Per_Month_Basic  Cost_Per_Month_Standard  Cost_Per_Month_Premium  \\\n",
+       "0                  9.03                    14.67                   20.32   \n",
        "1                 10.16                    15.24                   20.32   \n",
        "2                  4.61                     7.11                    9.96   \n",
        "3                  7.91                    11.87                   15.03   \n",
@@ -542,7 +542,7 @@
        "4  [5506.0, 3654.0, 1852.0]   15.197567  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -565,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -612,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
diff --git a/Notebooks/Spark-Example-22-Mllib-Clustering.ipynb b/Notebooks/Spark-Example-22-Mllib-Clustering.ipynb
index 2e1f7bd..4021816 100644
--- a/Notebooks/Spark-Example-22-Mllib-Clustering.ipynb
+++ b/Notebooks/Spark-Example-22-Mllib-Clustering.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -223,8 +223,8 @@
        "</div>"
       ],
       "text/plain": [
-       "   Country_Code         Country  Total_Library_Size  Num_TV_Shows  Num_Movies   \n",
-       "0            ar       Argentina                4760          3154        1606  \\\n",
+       "   Country_Code         Country  Total_Library_Size  Num_TV_Shows  Num_Movies  \\\n",
+       "0            ar       Argentina                4760          3154        1606   \n",
        "1            au       Australia                6114          4050        2064   \n",
        "2            at         Austria                5640          3779        1861   \n",
        "3            be         Belgium                4990          3374        1616   \n",
@@ -252,7 +252,7 @@
        "[65 rows x 8 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -340,12 +340,12 @@
        "</div>"
       ],
       "text/plain": [
-       "  Country_Code    Country  Total_Library_Size  Num_TV_Shows  Num_Movies   \n",
-       "0           ar  Argentina                4760          3154        1606  \\\n",
+       "  Country_Code    Country  Total_Library_Size  Num_TV_Shows  Num_Movies  \\\n",
+       "0           ar  Argentina                4760          3154        1606   \n",
        "1           au  Australia                6114          4050        2064   \n",
        "\n",
-       "   Cost_Per_Month_Basic  Cost_Per_Month_Standard  Cost_Per_Month_Premium   \n",
-       "0                  3.74                     6.30                    9.26  \\\n",
+       "   Cost_Per_Month_Basic  Cost_Per_Month_Standard  Cost_Per_Month_Premium  \\\n",
+       "0                  3.74                     6.30                    9.26   \n",
        "1                  7.84                    12.12                   16.39   \n",
        "\n",
        "                                       features  \n",
@@ -353,7 +353,7 @@
        "1  [6114.0, 4050.0, 2064.0, 7.84, 12.12, 16.39]  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -380,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -448,12 +448,12 @@
        "</div>"
       ],
       "text/plain": [
-       "  Country_Code    Country  Total_Library_Size  Num_TV_Shows  Num_Movies   \n",
-       "0           ar  Argentina                4760          3154        1606  \\\n",
+       "  Country_Code    Country  Total_Library_Size  Num_TV_Shows  Num_Movies  \\\n",
+       "0           ar  Argentina                4760          3154        1606   \n",
        "1           au  Australia                6114          4050        2064   \n",
        "\n",
-       "   Cost_Per_Month_Basic  Cost_Per_Month_Standard  Cost_Per_Month_Premium   \n",
-       "0                  3.74                     6.30                    9.26  \\\n",
+       "   Cost_Per_Month_Basic  Cost_Per_Month_Standard  Cost_Per_Month_Premium  \\\n",
+       "0                  3.74                     6.30                    9.26   \n",
        "1                  7.84                    12.12                   16.39   \n",
        "\n",
        "                                       features  prediction  \n",
@@ -461,7 +461,7 @@
        "1  [6114.0, 4050.0, 2064.0, 7.84, 12.12, 16.39]           1  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -480,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -549,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -574,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -605,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -642,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -660,9 +660,17 @@
     "plot_clustering(data,predictions)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bisecting K-Means\n",
+    "Bisecting K-Means is a specialized variant of the K-Means clustering algorithm, adapted to create a hierarchical cluster structure. The algorithm starts with one large cluster containing all data points and iteratively bisects the clusters using standard K-Means. This process continues until a predetermined number of $k$ leaf clusters are achieved or no clusters are left that can be divided further. The algorithm has been particularly optimized for parallel computation in PySpark's BisectingKMeans class, where bisecting steps for clusters at the same hierarchical level are grouped to enhance parallelism. If the bisecting process could potentially create more than $k$ leaf clusters, the algorithm gives priority to bisecting larger clusters."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -670,16 +678,16 @@
      "output_type": "stream",
      "text": [
       "Cluster 0\n",
-      "['Austria', 'Belgium', 'Canada', 'Estonia', 'Germany', 'Hungary', 'Iceland', 'Ireland', 'Italy', 'Latvia', 'Lithuania', 'Poland', 'Portugal', 'Spain', 'Sweden', 'United Kingdom']\n",
+      "['Austria', 'Belgium', 'Bulgaria', 'Canada', 'Croatia', 'Czechia', 'Estonia', 'Germany', 'Greece', 'Hungary', 'Iceland', 'Ireland', 'Italy', 'Latvia', 'Lithuania', 'Netherlands', 'Poland', 'Portugal', 'Romania', 'San Marino', 'Slovakia', 'Spain', 'Sweden', 'United Kingdom']\n",
       "Cluster 1\n",
-      "['Bulgaria', 'Croatia', 'Czechia', 'Greece', 'Netherlands', 'Romania', 'Slovakia']\n",
+      "['Argentina', 'Australia', 'Denmark', 'Finland', 'France', 'Gibraltar', 'Guatemala', 'Japan', 'Monaco', 'New Zealand', 'Norway', 'Paraguay', 'Philippines', 'Singapore', 'South Africa', 'South Korea', 'Switzerland', 'United States', 'Venezuela']\n",
       "Cluster 2\n",
-      "['San Marino']\n"
+      "['Bolivia', 'Brazil', 'Chile', 'Colombia', 'Costa Rica', 'Ecuador', 'Honduras', 'Hong Kong', 'India', 'Indonesia', 'Israel', 'Liechtenstein', 'Malaysia', 'Mexico', 'Moldova', 'Peru', 'Russia', 'Taiwan', 'Thailand', 'Turkey', 'Ukraine', 'Uruguay']\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x900 with 1 Axes>"
       ]
@@ -690,7 +698,7 @@
    ],
    "source": [
     "from pyspark.ml.clustering import BisectingKMeans\n",
-    "bkm =BisectingKMeans(featuresCol='features', k=5, distanceMeasure=\"cosine\") \n",
+    "bkm =BisectingKMeans(featuresCol='features', k=3, distanceMeasure=\"cosine\") \n",
     "model=bkm.fit(data)\n",
     "predictions=model.transform(data)\n",
     "for i in range(num_clusters):\n",
@@ -700,37 +708,6 @@
     "plot_clustering(data,predictions)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # Import TSNE\n",
-    "# from sklearn.manifold import TSNE\n",
-    "# # Create a TSNE instance: model\n",
-    "# model = TSNE(learning_rate=50)\n",
-    "# # Apply fit_transform to normalized_movements: tsne_features\n",
-    "# tsne_features = model.fit_transform(normalized_movements)\n",
-    "# # Select the 0th feature: xs\n",
-    "# xs = tsne_features[:,0]\n",
-    "# # Select the 1th feature: ys\n",
-    "# ys = tsne_features[:,1]\n",
-    "# # Scatter plot\n",
-    "# plt.scatter(xs,ys,alpha=0.5)\n",
-    "# # Annotate the points\n",
-    "# for x, y, company in zip(xs, ys, companies):\n",
-    "#     plt.annotate(company, (x, y), fontsize=5, alpha=0.75)\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/Notebooks/Spark-Example-23-Mllib-Sentiment Model.ipynb b/Notebooks/Spark-Example-23-Mllib-Sentiment Model.ipynb
index 5d2f7cc..d5eccec 100644
--- a/Notebooks/Spark-Example-23-Mllib-Sentiment Model.ipynb	
+++ b/Notebooks/Spark-Example-23-Mllib-Sentiment Model.ipynb	
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-----+\n",
+      "|text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |sentiment|label|\n",
+      "+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-----+\n",
+      "|One of the other reviewers has mentioned that after watching just 1 Oz episode you'll be hooked. They are right, as this is exactly what happened with me.<br /><br />The first thing that struck me about Oz was its brutality and unflinching scenes of violence, which set in right from the word GO. Trust me, this is not a show for the faint hearted or timid. This show pulls no punches with regards to drugs, sex or violence. Its is hardcore, in the classic use of the word.<br /><br />It is called OZ as that is the nickname given to the Oswald Maximum Security State Penitentary. It focuses mainly on Emerald City, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. Em City is home to many..Aryans, Muslims, gangstas, Latinos, Christians, Italians, Irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.<br /><br />I would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. Forget pretty pictures painted for mainstream audiences, forget charm, forget romance...OZ doesn't mess around. The first episode I ever saw struck me as so nasty it was surreal, I couldn't say I was ready for it, but as I watched more, I developed a taste for Oz, and got accustomed to the high levels of graphic violence. Not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) Watching Oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |positive |1    |\n",
+      "|This show was an amazing, fresh & innovative idea in the 70's when it first aired. The first 7 or 8 years were brilliant, but things dropped off after that. By 1990, the show was not really funny anymore, and it's continued its decline further to the complete waste of time it is today.<br /><br />It's truly disgraceful how far this show has fallen. The writing is painfully bad, the performances are almost as bad - if not for the mildly entertaining respite of the guest-hosts, this show probably wouldn't still be on the air. I find it so hard to believe that the same creator that hand-selected the original cast also chose the band of hacks that followed. How can one recognize such brilliance and then see fit to replace it with such mediocrity? I felt I must give 2 stars out of respect for the original cast that made this show such a huge success. As it is now, the show is just awful. I can't believe it's still on the air.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |negative |0    |\n",
+      "|So im not a big fan of Boll's work but then again not many are. I enjoyed his movie Postal (maybe im the only one). Boll apparently bought the rights to use Far Cry long ago even before the game itself was even finsished. <br /><br />People who have enjoyed killing mercs and infiltrating secret research labs located on a tropical island should be warned, that this is not Far Cry... This is something Mr Boll have schemed together along with his legion of schmucks.. Feeling loneley on the set Mr Boll invites three of his countrymen to play with. These players go by the names of Til Schweiger, Udo Kier and Ralf Moeller.<br /><br />Three names that actually have made them selfs pretty big in the movie biz. So the tale goes like this, Jack Carver played by Til Schweiger (yes Carver is German all hail the bratwurst eating dudes!!) However I find that Tils acting in this movie is pretty badass.. People have complained about how he's not really staying true to the whole Carver agenda but we only saw carver in a first person perspective so we don't really know what he looked like when he was kicking a**.. <br /><br />However, the storyline in this film is beyond demented. We see the evil mad scientist Dr. Krieger played by Udo Kier, making Genetically-Mutated-soldiers or GMS as they are called. Performing his top-secret research on an island that reminds me of SPOILER Vancouver for some reason. Thats right no palm trees here. Instead we got some nice rich lumberjack-woods. We haven't even gone FAR before I started to CRY (mehehe) I cannot go on any more.. If you wanna stay true to Bolls shenanigans then go and see this movie you will not be disappointed it delivers the true Boll experience, meaning most of it will suck.<br /><br />There are some things worth mentioning that would imply that Boll did a good work on some areas of the film such as some nice boat and fighting scenes. Until the whole cromed/albino GMS squad enters the scene and everything just makes me laugh.. The movie Far Cry reeks of scheisse (that's poop for you simpletons) from a fa,r if you wanna take a wiff go ahead.. BTW Carver gets a very annoying sidekick who makes you wanna shoot him the first three minutes he's on screen.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |negative |0    |\n",
+      "|The Karen Carpenter Story shows a little more about singer Karen Carpenter's complex life. Though it fails in giving accurate facts, and details.<br /><br />Cynthia Gibb (portrays Karen) was not a fine election. She is a good actress , but plays a very naive and sort of dumb Karen Carpenter. I think that the role needed a stronger character. Someone with a stronger personality.<br /><br />Louise Fletcher role as Agnes Carpenter is terrific, she does a great job as Karen's mother.<br /><br />It has great songs, which could have been included in a soundtrack album. Unfortunately they weren't, though this movie was on the top of the ratings in USA and other several countries                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |positive |1    |\n",
+      "|The Cell is an exotic masterpiece, a dizzying trip into not only the vast mind of a serial killer, but also into one of a very talented director. This is conclusive evidence of what can be achieved if human beings unleash their uninhibited imaginations. This is boldness at work, pushing aside thoughts to fall into formulas and cliches and creating something truly magnificent. This is the best movie of the year to date.<br /><br />I've read numerous complaints about this film, anywhere from all style and no substance to poorly cast characters and bad acting. To negatively criticize this film is to miss the point. This movie may be a landmark, a tradition where future movies will hopefully follow. The Cell has just opened the door to another world of imagination. So can we slam the door in its face and tell it and its director Tarsem Singh that we don't want any more? Personally, I would more than welcome another movie by Tarsem, and would love to see someone try to challenge him.<br /><br />We've all heard talk about going inside the mind of a serial killer, and yes, I do agree that the genre is a bit overworked. The 90s were full of movies trying to depict what makes serial killers tick; some of them worked, but most failed. But The Cell does not blaze down the same trail, we are given a new twist, we are physically transported into the mind and presented with nothing less than a fascinating journey of the most mysterious subject matter ever studied.<br /><br />I like how the movie does not bog us down with too much scientific jargon trying to explain how Jennifer Lopez actually gets to enter the brain of another. Instead, she just lies down on a laboratory table and is wrapped with what looks like really long Twizzlers and jaunted into another entity. The Cell wants to let you see what it's all about and not how it's all about, and I guess that's what some people don't like. True, I do like explanations with my movies, but when a movie ventures onto new ground you must let it do what it desires and simply take it in.<br /><br />I noticed how the film was very dark when it showed reality, maybe to contrast the bright visuals when inside the brain of another. Nonetheless, the set design was simply astonishing. I wouldn't be surprised if this film took home a few Oscars in cinematography, best costumes, best director and the like. If it were up to me it'd at least get nominated for best picture.<br /><br />I've noticed that I've kind of been repeating myself. Not because there's nothing else to say, but because I can't stress enough how fantastic I thought The Cell was. If you walk into the movie with a very open mind and to have it taken over with wonders and an eye-popping feast then you are assured a good time. I guess this film was just a little too much for some people, writing it off as weird or crazy. I am very much into psychology and the imagination of the human mind, so it was right down my alley. Leaving the theater, I heard one audience member say Whoever made that movie sure did a lot of good drugs. If so, I want what he was smoking.<br /><br />**** (out of 4)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |positive |1    |\n",
+      "|'War movie' is a Hollywood genre that has been done and redone so many times that clichéd dialogue, rehashed plot and over-the-top action sequences seem unavoidable for any conflict dealing with large-scale combat. Once in a while, however, a war movie comes along that goes against the grain and brings a truly original and compelling story to life on the silver screen. The Civil War-era Cold Mountain, starring Jude Law, Nicole Kidman and Renée Zellweger is such a film.<br /><br />Then again, calling Cold Mountain a war movie is not entirely accurate. True enough, the film opens with a (quite literally) quick-and-dirty battle sequence that puts Glory director Edward Zwick shame. However, Cold Mountain is not so much about the Civil War itself as it is about the period and the people of the times. The story centers around disgruntled Confederate soldier Inman, played by Jude Law, who becomes disgusted with the gruesome war and homesick for the beautiful hamlet of Cold Mountain, North Carolina and the equally beautiful southern belle he left behind, Ada Monroe, played by Nicole Kidman. At first glance, this setup appears formulaic as the romantic interest back home gives the audience enough sympathy to root for the reluctant soldier's tribulations on the battlefield. Indeed, the earlier segments of the film are relatively unimpressive and even somewhat contrived.<br /><br />Cold Mountain soon takes a drastic turn, though, as the intrepid hero Inman turns out to be a deserter (incidentally saving the audience from the potentially confusing scenario of wanting to root for the Confederates) and begins a long odyssey homeward. Meanwhile, back at the farm, Ada's cultured ways prove of little use in the fields; soon she is transformed into something of a wilderbeast. Coming to Ada's rescue is the course, tough-as-nails Ruby Thewes, played by Renée Zellweger, who helps Ada put the farm back together and, perhaps more importantly, cope with the loneliness and isolation the war seems to have brought upon Ada.<br /><br />Within these two settings, a vivid, compelling and, at times, very disturbing portrait of the war-torn South unfolds. The characters with whom Inman and Ada interact are surprisingly complex, enhanced by wonderful performances of Brendan Gleeson as Ruby's deadbeat father, Ray Winstone as an unrepentant southern lawman, and Natalie Portman as a deeply troubled and isolated young mother. All have been greatly affected and changed by the war of Northern aggression, mostly for the worse. The dark, pervading anti-war message, accented by an effective, haunting score and chillingly beautiful shots of Virginia and North Carolina, is communicated to the audience not so much by gruesome battle scenes as by the scarred land and traumatized people for which the war was fought. Though the weapons and tactics of war itself have changed much in the past century, it's hellish effect on the land is timelessly relevant.<br /><br />Director Anthony Minghella manages to maintain this gloomy mood for most of the film, but the atmosphere is unfortunately denigrated by a rather tepid climax that does little justice to the wonderfully formed characters. The love story between Inman and Ada is awkwardly tacked onto the beginning and end of the film, though the inherently distant, abstracted and even absurd nature of their relationship in a way fits the dismal nature of the rest of the plot.<br /><br />Make no mistake, Cold Mountain has neither the traits of a feel-good romance nor an inspiring war drama. It is a unique vision of an era that is sure not only to entertain but also to truly absorb the audience into the lives of a people torn apart by a war and entirely desperate to be rid of its terrible repercussions altogether.                                                                                                                                                                                                                                     |positive |1    |\n",
+      "|My first exposure to the Templarios & not a good one. I was excited to find this title among the offerings from Anchor Bay Video, which has brought us other cult classics such as Spider Baby. The print quality is excellent, but this alone can't hide the fact that the film is deadly dull. There's a thrilling opening sequence in which the villagers exact a terrible revenge on the Templars (& set the whole thing in motion), but everything else in the movie is slow, ponderous &, ultimately, unfulfilling. Adding insult to injury: the movie was dubbed, not subtitled, as promised on the video jacket.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |negative |0    |\n",
+      "|One of the most significant quotes from the entire film is pronounced halfway through by the protagonist, the mafia middle-man Titta Di Girolamo, a physically non-descript, middle-aged man originally from Salerno in Southern Italy. When we're introduced to him at the start of the film, he's been living a non-life in an elegant but sterile hotel in the Italian-speaking Canton of Switzerland for the last ten years, conducting a business we are only gradually introduced to. While this pivotal yet apparently unremarkable scene takes place employees of the the Swiss bank who normally count Di Girolamo's cash tell him that 10,000 dollars are missing from his usual suitcase full of tightly stacked banknotes. At the news, he quietly but icily threatens his coaxing bank manager of wanting to close down his account. Meanwhile he tells us, the spectators, that when you bluff, you have to bluff right through to the end without fear of being caught out or appearing ridiculous. He says: you can't bluff for a while and then halfway through, tell the truth. Having eventually done this - bluffed only halfway through and told the truth, and having accepted the consequences of life and ultimately, love - is exactly the reason behind the beginning of Titta Di Girolamo's troubles. <br /><br />This initially unsympathetic character, a scowling, taciturn, curt man on the verge of 50, a man who won't even reply in kind to chambermaids and waitresses who say hello and goodbye, becomes at one point someone the spectator cares deeply about. At one point in his non-life, Titta decides to feel concern about appearing ridiculous. The first half of the film may be described as slow by some. It does indeed reveal Di Girolamo's days and nights in that hotel at an oddly disjoined, deliberate pace, revealing seemingly mundane and irrelevant details. However, scenes that may have seemed unnecessary reveal just how essential they are as this masterfully constructed and innovative film unfolds before your eyes. The existence of Titta Di Girolamo - the man with no imagination, identity or life, the unsympathetic character you unexpectedly end up loving and feeling for when you least thought you would - is also conveyed with elegantly edited sequences and very interesting use of music (one theme by the Scottish band Boards of Canada especially stood out). <br /><br />Never was the contrast between the way Hollywood and Italy treat mobsters more at odds than since the release of films such as Le Conseguenze dell'Amore or L'Imbalsamatore. Another interesting element was the way in which the film made use of the protagonist's insomnia. Not unlike The Machinist (and in a far more explicit way, the Al Pacino film Insomnia), Le Conseguenze dell'Amore uses this condition to symbolise a deeper emotional malaise that's been rammed so deep into the obscurity of the unconscious, it's almost impossible to pin-point its cause (if indeed there is one). <br /><br />The young and sympathetic hotel waitress Sofia (played by Olivia Magnani, grand-daughter of the legendary Anna) and the memory of Titta's best friend, a man whom he hasn't seen in 20 years, unexpectedly provide a tiny window onto life that Titta eventually (though tentatively at first) accepts to look through again. Though it's never explicitly spelt out, the spectator KNOWS that to a man like Titta, accepting The Consequences of Love will have unimaginable consequences. A film without a single scene of sex or violence, a film that unfolds in its own time and concedes nothing to the spectator's expectations, Le Conseguenze dell'Amore is a fine representative of that small, quiet, discreet Renaissance that has been taking place in Italian cinema since the decline of Cinecittà during the second half of the 70s. The world is waiting for Italy to produce more Il Postino-like fare, more La Vita è Bella-style films... neglecting to explore fine creations like Le Conseguenze dell'Amore, L'Imbalsamatore and others. Your loss, world.|positive |1    |\n",
+      "|This movie is based on the book, A Many Splendored Thing by Han Suyin and tackles issues of race relations between Asians and Whites, a topic that comes from Han's personal experiences as an Eurasian growing up in China. That background, and the beautiful Hong Kong settings, gives this love story a unique and rather daring atmosphere for its time.<br /><br />Other than that, the story is a stereotypical romance with a memorable song that is perhaps more remembered than the movie itself. The beautiful Jennifer Jones looks the part and gives a wonderful, Oscar nominated performance as a doctor of mixed breed during the advent of Communism in mainland China. William Holden never looked better playing a romantic lead as a journalist covering war torn regions in the world. The acting is top notch, and the chemistry between the two lovers provides for some genuine moments of silver screen affection sure to melt the hearts of those who are romantically inclined.<br /><br />The cinematography really brings out fifty's Hong Kong, especially the hilltop overlooking the harbor where the two lovers spend their most intimate moments. The ending is a real tear-jerker. Some may consider sentimental romances passé, but, for those who enjoy classic Hollywood love stories, this is a shining example.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |positive |1    |\n",
+      "|Of all the films I have seen, this one, The Rage, has got to be one of the worst yet. The direction, LOGIC, continuity, changes in plot-script and dialog made me cry out in pain. How could ANYONE come up with something so crappy? Gary Busey is know for his B movies, but this is a sure W movie. (W=waste).<br /><br />Take for example: about two dozen FBI & local law officers surround a trailer house with a jeep wagoneer. Inside the jeep is MA and is confused as to why all the cops are about. Within seconds a huge gun battle ensues, MA being killed straight off. The cops blast away at the jeep with gary and company blasting away at them. The cops fall like dominoes and the jeep with Gary drives around in circles and are not hit by one single bullet/pellet. MA is killed and gary seems to not to have noticed-damn that guy is tough. Truly a miracle, not since the six-shooter held 300 bullets has there been such a miracle.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |negative |0    |\n",
+      "+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-----+\n",
+      "only showing top 10 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df0.show(10, truncate=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -172,8 +204,8 @@
        "</div>"
       ],
       "text/plain": [
-       "                                                    text sentiment  label   \n",
-       "12643  To be hones, I used to like this show and watc...  negative      0  \\\n",
+       "                                                    text sentiment  label  \\\n",
+       "12643  To be hones, I used to like this show and watc...  negative      0   \n",
        "12644  This movie is a disgrace to the Major League F...  negative      0   \n",
        "12645  John Garfield plays a Marine who is blinded by...  positive      1   \n",
        "12646  Bad plot, bad dialogue, bad acting, idiotic di...  negative      0   \n",
@@ -187,7 +219,7 @@
        "12647   going  have  disagree with the previous comme...  "
       ]
      },
-     "execution_count": 30,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -217,120 +249,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>text</th>\n",
-       "      <th>sentiment</th>\n",
-       "      <th>label</th>\n",
-       "      <th>text_c</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>12643</th>\n",
-       "      <td>To be hones, I used to like this show and watc...</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>0</td>\n",
-       "      <td>hone   use   like this show and watch   reg...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12644</th>\n",
-       "      <td>This movie is a disgrace to the Major League F...</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>0</td>\n",
-       "      <td>this movie    disgrace   the Major League Fran...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12645</th>\n",
-       "      <td>John Garfield plays a Marine who is blinded by...</td>\n",
-       "      <td>positive</td>\n",
-       "      <td>1</td>\n",
-       "      <td>John Garfield play   Marine who   blind    gre...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12646</th>\n",
-       "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>0</td>\n",
-       "      <td>bad plot bad dialogue bad act idiotic direct t...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12647</th>\n",
-       "      <td>I'm going to have to disagree with the previou...</td>\n",
-       "      <td>negative</td>\n",
-       "      <td>0</td>\n",
-       "      <td>go   have   disagree with the previous comme...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    text sentiment  label   \n",
-       "12643  To be hones, I used to like this show and watc...  negative      0  \\\n",
-       "12644  This movie is a disgrace to the Major League F...  negative      0   \n",
-       "12645  John Garfield plays a Marine who is blinded by...  positive      1   \n",
-       "12646  Bad plot, bad dialogue, bad acting, idiotic di...  negative      0   \n",
-       "12647  I'm going to have to disagree with the previou...  negative      0   \n",
-       "\n",
-       "                                                  text_c  \n",
-       "12643     hone   use   like this show and watch   reg...  \n",
-       "12644  this movie    disgrace   the Major League Fran...  \n",
-       "12645  John Garfield play   Marine who   blind    gre...  \n",
-       "12646  bad plot bad dialogue bad act idiotic direct t...  \n",
-       "12647    go   have   disagree with the previous comme...  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import spacy\n",
-    "from pyspark.sql.functions import udf\n",
-    "from pyspark.sql.types import StringType\n",
+    "# import spacy\n",
+    "# from pyspark.sql.functions import udf\n",
+    "# from pyspark.sql.types import StringType\n",
     "\n",
-    "# Load the spaCy model\n",
-    "nlp = spacy.load(\"en_core_web_sm\", disable=[\"parser\", \"ner\"])\n",
+    "# # Load the spaCy model\n",
+    "# nlp = spacy.load(\"en_core_web_sm\", disable=[\"parser\", \"ner\"])\n",
     "\n",
-    "# Define a function to apply the lemmatizer to a text\n",
-    "def lemmatize_text(text):\n",
-    "    doc = nlp(text)\n",
-    "    lemmas = [token.lemma_ for token in doc]\n",
-    "    return \" \".join(lemmas)\n",
+    "# # Define a function to apply the lemmatizer to a text\n",
+    "# def lemmatize_text(text):\n",
+    "#     doc = nlp(text)\n",
+    "#     lemmas = [token.lemma_ for token in doc]\n",
+    "#     return \" \".join(lemmas)\n",
     "\n",
-    "# Define a UDF to apply the lemmatizer to a column\n",
-    "lemmatize_udf = udf(lemmatize_text, StringType())\n",
+    "# # Define a UDF to apply the lemmatizer to a column\n",
+    "# lemmatize_udf = udf(lemmatize_text, StringType())\n",
     "\n",
-    "# Apply the UDF to a DataFrame column\n",
-    "df0 = df0.withColumn(\"text_c\", lemmatize_udf(df0[\"text_c\"]))\n",
+    "# # Apply the UDF to a DataFrame column\n",
+    "# df0 = df0.withColumn(\"text_c\", lemmatize_udf(df0[\"text_c\"]))\n",
     "\n",
-    "# Caching must be used !!!!!!\n",
-    "df0 = df0.cache()\n",
-    "df0.toPandas().tail(5)"
+    "# # Caching must be used !!!!!!\n",
+    "# df0 = df0.cache()\n",
+    "# df0.toPandas().tail(5)"
    ]
   },
   {
@@ -343,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -390,14 +334,17 @@
    "source": [
     "# Create a weight of each class\n",
     "from pyspark.sql import functions as F\n",
-    "p_weight = df.filter('label == 1').count()/ df.count()\n",
-    "n_weight = df.filter('label == 0').count()/ df.count()\n",
+    "size = df.count()\n",
+    "number_of_1 = df.filter('label == 1').count()\n",
+    "number_of_0 = size - number_of_1\n",
+    "p_weight = number_of_1/ size\n",
+    "n_weight = number_of_0/ size\n",
     "print(n_weight, p_weight)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -407,11 +354,11 @@
       "+--------------------+---------+-----+--------------------+------------------+\n",
       "|                text|sentiment|label|              text_c|            weight|\n",
       "+--------------------+---------+-----+--------------------+------------------+\n",
-      "| Så som i himmele...| positive|    1|   som   himmelen...|0.5060074847350797|\n",
-      "| While sporadical...| negative|    0|  while sporadica...|0.4939925152649202|\n",
+      "| Så som i himmele...| positive|    1|  som  himmelen  ...|0.5060074847350797|\n",
+      "| While sporadical...| negative|    0| While sporadical...|0.4939925152649202|\n",
       "|'Blue Desert' may...| negative|    0|Blue Desert may h...|0.4939925152649202|\n",
-      "|'Checking Out' is...| positive|    1|check out    extr...|0.5060074847350797|\n",
-      "|'Presque Rien' ('...| positive|    1|presque Rien come...|0.5060074847350797|\n",
+      "|'Checking Out' is...| positive|    1|Checking Out   ex...|0.5060074847350797|\n",
+      "|'Presque Rien' ('...| positive|    1|Presque Rien Come...|0.5060074847350797|\n",
       "+--------------------+---------+-----+--------------------+------------------+\n",
       "only showing top 5 rows\n",
       "\n"
@@ -425,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -477,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -528,110 +475,110 @@
        "      <td>Så som i himmelen  .. as above so below.. tha...</td>\n",
        "      <td>positive</td>\n",
        "      <td>1</td>\n",
-       "      <td>som   himmelen     above   below that very ...</td>\n",
+       "      <td>som  himmelen    above  below that very spec...</td>\n",
        "      <td>0.506007</td>\n",
-       "      <td>[, , , som, , , himmelen, , , , , above, , , b...</td>\n",
-       "      <td>[, , , som, , , himmelen, , , , , , , special,...</td>\n",
-       "      <td>(77.0, 0.0, 4.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
-       "      <td>(0.015166436921203202, 0.0, 2.147722756669075,...</td>\n",
+       "      <td>[, , som, , himmelen, , , , above, , below, th...</td>\n",
+       "      <td>[, , som, , himmelen, , , , , special, point, ...</td>\n",
+       "      <td>(43.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>(0.008469568670282307, 0.0, 2.4572384294370635...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>While sporadically engrossing (including a fe...</td>\n",
        "      <td>negative</td>\n",
        "      <td>0</td>\n",
-       "      <td>while sporadically engross include   few eff...</td>\n",
+       "      <td>While sporadically engrossing including  few ...</td>\n",
        "      <td>0.493993</td>\n",
-       "      <td>[, , while, sporadically, engross, include, , ...</td>\n",
-       "      <td>[, , sporadically, engross, include, , , effec...</td>\n",
-       "      <td>(72.0, 0.0, 3.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0,...</td>\n",
-       "      <td>(0.014181603354891307, 0.0, 1.6107920675018061...</td>\n",
+       "      <td>[, while, sporadically, engrossing, including,...</td>\n",
+       "      <td>[, sporadically, engrossing, including, , effe...</td>\n",
+       "      <td>(38.0, 0.0, 3.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>(0.0074847351039704114, 0.0, 1.842928822077797...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>'Blue Desert' may have had the potential to be...</td>\n",
        "      <td>negative</td>\n",
        "      <td>0</td>\n",
-       "      <td>Blue Desert may have have the potential    eve...</td>\n",
+       "      <td>Blue Desert may have had the potential   even ...</td>\n",
        "      <td>0.493993</td>\n",
-       "      <td>[blue, desert, may, have, have, the, potential...</td>\n",
-       "      <td>[blue, desert, may, potential, , , , even, , ,...</td>\n",
-       "      <td>(93.0, 2.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,...</td>\n",
-       "      <td>(0.01831790433340127, 0.8910976455736771, 0.53...</td>\n",
+       "      <td>[blue, desert, may, have, had, the, potential,...</td>\n",
+       "      <td>[blue, desert, may, potential, , , even, , hal...</td>\n",
+       "      <td>(52.0, 2.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0,...</td>\n",
+       "      <td>(0.010242269089643721, 1.0085643367770025, 0.6...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>'Checking Out' is an extraordinary film that t...</td>\n",
        "      <td>positive</td>\n",
        "      <td>1</td>\n",
-       "      <td>check out    extraordinary film that tower abo...</td>\n",
+       "      <td>Checking Out   extraordinary film that towers ...</td>\n",
        "      <td>0.506007</td>\n",
-       "      <td>[check, out, , , , extraordinary, film, that, ...</td>\n",
-       "      <td>[check, , , , extraordinary, film, tower, film...</td>\n",
-       "      <td>(40.0, 0.0, 3.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
-       "      <td>(0.00787866853049517, 0.0, 1.6107920675018061,...</td>\n",
+       "      <td>[checking, out, , , extraordinary, film, that,...</td>\n",
+       "      <td>[checking, , , extraordinary, film, towers, fi...</td>\n",
+       "      <td>(21.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0,...</td>\n",
+       "      <td>(0.004136300978509964, 0.0, 1.8429288220777975...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>'Presque Rien' ('Come Undone') is an earlier w...</td>\n",
        "      <td>positive</td>\n",
        "      <td>1</td>\n",
-       "      <td>presque Rien come Undone    early work   the i...</td>\n",
+       "      <td>Presque Rien Come Undone   earlier work  the i...</td>\n",
        "      <td>0.506007</td>\n",
-       "      <td>[presque, rien, come, undone, , , , early, wor...</td>\n",
-       "      <td>[presque, rien, come, undone, , , , early, wor...</td>\n",
-       "      <td>(232.0, 0.0, 6.0, 3.0, 0.0, 2.0, 0.0, 0.0, 0.0...</td>\n",
-       "      <td>(0.04569627747687199, 0.0, 3.2215841350036123,...</td>\n",
+       "      <td>[presque, rien, come, undone, , , earlier, wor...</td>\n",
+       "      <td>[presque, rien, come, undone, , , earlier, wor...</td>\n",
+       "      <td>(123.0, 0.0, 6.0, 3.0, 0.0, 0.0, 0.0, 8.0, 0.0...</td>\n",
+       "      <td>(0.024226905731272647, 0.0, 3.685857644155595,...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                text sentiment  label   \n",
-       "0   Så som i himmelen  .. as above so below.. tha...  positive      1  \\\n",
+       "                                                text sentiment  label  \\\n",
+       "0   Så som i himmelen  .. as above so below.. tha...  positive      1   \n",
        "1   While sporadically engrossing (including a fe...  negative      0   \n",
        "2  'Blue Desert' may have had the potential to be...  negative      0   \n",
        "3  'Checking Out' is an extraordinary film that t...  positive      1   \n",
        "4  'Presque Rien' ('Come Undone') is an earlier w...  positive      1   \n",
        "\n",
-       "                                              text_c    weight   \n",
-       "0     som   himmelen     above   below that very ...  0.506007  \\\n",
-       "1    while sporadically engross include   few eff...  0.493993   \n",
-       "2  Blue Desert may have have the potential    eve...  0.493993   \n",
-       "3  check out    extraordinary film that tower abo...  0.506007   \n",
-       "4  presque Rien come Undone    early work   the i...  0.506007   \n",
+       "                                              text_c    weight  \\\n",
+       "0    som  himmelen    above  below that very spec...  0.506007   \n",
+       "1   While sporadically engrossing including  few ...  0.493993   \n",
+       "2  Blue Desert may have had the potential   even ...  0.493993   \n",
+       "3  Checking Out   extraordinary film that towers ...  0.506007   \n",
+       "4  Presque Rien Come Undone   earlier work  the i...  0.506007   \n",
        "\n",
-       "                                               words   \n",
-       "0  [, , , som, , , himmelen, , , , , above, , , b...  \\\n",
-       "1  [, , while, sporadically, engross, include, , ...   \n",
-       "2  [blue, desert, may, have, have, the, potential...   \n",
-       "3  [check, out, , , , extraordinary, film, that, ...   \n",
-       "4  [presque, rien, come, undone, , , , early, wor...   \n",
+       "                                               words  \\\n",
+       "0  [, , som, , himmelen, , , , above, , below, th...   \n",
+       "1  [, while, sporadically, engrossing, including,...   \n",
+       "2  [blue, desert, may, have, had, the, potential,...   \n",
+       "3  [checking, out, , , extraordinary, film, that,...   \n",
+       "4  [presque, rien, come, undone, , , earlier, wor...   \n",
        "\n",
-       "                                            filtered   \n",
-       "0  [, , , som, , , himmelen, , , , , , , special,...  \\\n",
-       "1  [, , sporadically, engross, include, , , effec...   \n",
-       "2  [blue, desert, may, potential, , , , even, , ,...   \n",
-       "3  [check, , , , extraordinary, film, tower, film...   \n",
-       "4  [presque, rien, come, undone, , , , early, wor...   \n",
+       "                                            filtered  \\\n",
+       "0  [, , som, , himmelen, , , , , special, point, ...   \n",
+       "1  [, sporadically, engrossing, including, , effe...   \n",
+       "2  [blue, desert, may, potential, , , even, , hal...   \n",
+       "3  [checking, , , extraordinary, film, towers, fi...   \n",
+       "4  [presque, rien, come, undone, , , earlier, wor...   \n",
        "\n",
-       "                                         rawFeatures   \n",
-       "0  (77.0, 0.0, 4.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0,...  \\\n",
-       "1  (72.0, 0.0, 3.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0,...   \n",
-       "2  (93.0, 2.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,...   \n",
-       "3  (40.0, 0.0, 3.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0,...   \n",
-       "4  (232.0, 0.0, 6.0, 3.0, 0.0, 2.0, 0.0, 0.0, 0.0...   \n",
+       "                                         rawFeatures  \\\n",
+       "0  (43.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "1  (38.0, 0.0, 3.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0,...   \n",
+       "2  (52.0, 2.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0,...   \n",
+       "3  (21.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0,...   \n",
+       "4  (123.0, 0.0, 6.0, 3.0, 0.0, 0.0, 0.0, 8.0, 0.0...   \n",
        "\n",
        "                                         featuresIDF  \n",
-       "0  (0.015166436921203202, 0.0, 2.147722756669075,...  \n",
-       "1  (0.014181603354891307, 0.0, 1.6107920675018061...  \n",
-       "2  (0.01831790433340127, 0.8910976455736771, 0.53...  \n",
-       "3  (0.00787866853049517, 0.0, 1.6107920675018061,...  \n",
-       "4  (0.04569627747687199, 0.0, 3.2215841350036123,...  "
+       "0  (0.008469568670282307, 0.0, 2.4572384294370635...  \n",
+       "1  (0.0074847351039704114, 0.0, 1.842928822077797...  \n",
+       "2  (0.010242269089643721, 1.0085643367770025, 0.6...  \n",
+       "3  (0.004136300978509964, 0.0, 1.8429288220777975...  \n",
+       "4  (0.024226905731272647, 0.0, 3.685857644155595,...  "
       ]
      },
-     "execution_count": 39,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -644,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -684,105 +631,105 @@
        "      <td>'Don't Look In the Basement' is so easy to kno...</td>\n",
        "      <td>positive</td>\n",
        "      <td>1</td>\n",
-       "      <td>do not look   the Basement    easy   knock but...</td>\n",
-       "      <td>[do, not, look, , , the, basement, , , , easy,...</td>\n",
-       "      <td>[look, , , basement, , , , easy, , , knock, tr...</td>\n",
-       "      <td>(63.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0,...</td>\n",
-       "      <td>(0.012408902935529893, 0.0, 0.5369306891672687...</td>\n",
+       "      <td>Dont Look  the Basement   easy  knock but the ...</td>\n",
+       "      <td>[dont, look, , the, basement, , , easy, , knoc...</td>\n",
+       "      <td>[dont, look, , basement, , , easy, , knock, tr...</td>\n",
+       "      <td>(33.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0,...</td>\n",
+       "      <td>(0.006499901537658516, 0.0, 0.0, 0.59092304052...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>*Flat SPOILERS* &lt;br /&gt;&lt;br /&gt;Five med students,...</td>\n",
        "      <td>positive</td>\n",
        "      <td>1</td>\n",
-       "      <td>flat spoiler five med student Nelson Kiefer Su...</td>\n",
-       "      <td>[flat, spoiler, five, med, student, nelson, ki...</td>\n",
-       "      <td>[flat, spoiler, five, med, student, nelson, ki...</td>\n",
-       "      <td>(67.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 0.0, 2.0,...</td>\n",
-       "      <td>(0.01319676978857941, 0.8910976455736771, 0.0,...</td>\n",
+       "      <td>Flat SPOILERS Five med students Nelson Kiefer ...</td>\n",
+       "      <td>[flat, spoilers, five, med, students, nelson, ...</td>\n",
+       "      <td>[flat, spoilers, five, med, students, nelson, ...</td>\n",
+       "      <td>(35.0, 1.0, 0.0, 1.0, 1.0, 0.0, 2.0, 1.0, 0.0,...</td>\n",
+       "      <td>(0.006893834964183274, 0.5042821683885013, 0.0...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>.... may seem far fetched.... but there really...</td>\n",
        "      <td>negative</td>\n",
        "      <td>0</td>\n",
-       "      <td>may seem far fetched but there really be   r...</td>\n",
-       "      <td>[, , may, seem, far, fetched, but, there, real...</td>\n",
-       "      <td>[, , may, seem, far, fetched, really, , , real...</td>\n",
-       "      <td>(69.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,...</td>\n",
-       "      <td>(0.013590703215104168, 0.0, 0.0, 0.0, 0.0, 0.6...</td>\n",
+       "      <td>may seem far fetched but there really was  re...</td>\n",
+       "      <td>[, may, seem, far, fetched, but, there, really...</td>\n",
+       "      <td>[, may, seem, far, fetched, really, , real, li...</td>\n",
+       "      <td>(38.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0,...</td>\n",
+       "      <td>(0.0074847351039704114, 0.0, 0.0, 0.0, 0.79757...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>...Our the grandpa's hour.&lt;br /&gt;&lt;br /&gt;More tha...</td>\n",
        "      <td>positive</td>\n",
        "      <td>1</td>\n",
-       "      <td>our the grandpa hourMore than the gangster its...</td>\n",
-       "      <td>[our, the, grandpa, hourmore, than, the, gangs...</td>\n",
-       "      <td>[grandpa, hourmore, gangster, , , detailed, de...</td>\n",
-       "      <td>(36.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0,...</td>\n",
-       "      <td>(0.007090801677445653, 0.0, 0.5369306891672687...</td>\n",
+       "      <td>Our the grandpas hourMore than the gangsters i...</td>\n",
+       "      <td>[our, the, grandpas, hourmore, than, the, gang...</td>\n",
+       "      <td>[grandpas, hourmore, gangsters, , detailed, de...</td>\n",
+       "      <td>(19.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0,...</td>\n",
+       "      <td>(0.0037423675519852057, 0.0, 0.614309607359265...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>...but I regret having seen it. Since the rati...</td>\n",
        "      <td>negative</td>\n",
        "      <td>0</td>\n",
-       "      <td>but   regret have see   since the rating   IMD...</td>\n",
-       "      <td>[but, , , regret, have, see, , , since, the, r...</td>\n",
-       "      <td>[, , regret, see, , , since, rating, , , imdb,...</td>\n",
-       "      <td>(75.0, 2.0, 2.0, 1.0, 1.0, 0.0, 2.0, 0.0, 0.0,...</td>\n",
-       "      <td>(0.014772503494678443, 0.8910976455736771, 1.0...</td>\n",
+       "      <td>but  regret having seen  Since the ratings  IM...</td>\n",
+       "      <td>[but, , regret, having, seen, , since, the, ra...</td>\n",
+       "      <td>[, regret, seen, , since, ratings, , imdb, rel...</td>\n",
+       "      <td>(42.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
+       "      <td>(0.008272601957019928, 0.0, 0.6143096073592659...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                text sentiment  label   \n",
-       "0  'Don't Look In the Basement' is so easy to kno...  positive      1  \\\n",
+       "                                                text sentiment  label  \\\n",
+       "0  'Don't Look In the Basement' is so easy to kno...  positive      1   \n",
        "1  *Flat SPOILERS* <br /><br />Five med students,...  positive      1   \n",
        "2  .... may seem far fetched.... but there really...  negative      0   \n",
        "3  ...Our the grandpa's hour.<br /><br />More tha...  positive      1   \n",
        "4  ...but I regret having seen it. Since the rati...  negative      0   \n",
        "\n",
-       "                                              text_c   \n",
-       "0  do not look   the Basement    easy   knock but...  \\\n",
-       "1  flat spoiler five med student Nelson Kiefer Su...   \n",
-       "2    may seem far fetched but there really be   r...   \n",
-       "3  our the grandpa hourMore than the gangster its...   \n",
-       "4  but   regret have see   since the rating   IMD...   \n",
+       "                                              text_c  \\\n",
+       "0  Dont Look  the Basement   easy  knock but the ...   \n",
+       "1  Flat SPOILERS Five med students Nelson Kiefer ...   \n",
+       "2   may seem far fetched but there really was  re...   \n",
+       "3  Our the grandpas hourMore than the gangsters i...   \n",
+       "4  but  regret having seen  Since the ratings  IM...   \n",
        "\n",
-       "                                               words   \n",
-       "0  [do, not, look, , , the, basement, , , , easy,...  \\\n",
-       "1  [flat, spoiler, five, med, student, nelson, ki...   \n",
-       "2  [, , may, seem, far, fetched, but, there, real...   \n",
-       "3  [our, the, grandpa, hourmore, than, the, gangs...   \n",
-       "4  [but, , , regret, have, see, , , since, the, r...   \n",
+       "                                               words  \\\n",
+       "0  [dont, look, , the, basement, , , easy, , knoc...   \n",
+       "1  [flat, spoilers, five, med, students, nelson, ...   \n",
+       "2  [, may, seem, far, fetched, but, there, really...   \n",
+       "3  [our, the, grandpas, hourmore, than, the, gang...   \n",
+       "4  [but, , regret, having, seen, , since, the, ra...   \n",
        "\n",
-       "                                            filtered   \n",
-       "0  [look, , , basement, , , , easy, , , knock, tr...  \\\n",
-       "1  [flat, spoiler, five, med, student, nelson, ki...   \n",
-       "2  [, , may, seem, far, fetched, really, , , real...   \n",
-       "3  [grandpa, hourmore, gangster, , , detailed, de...   \n",
-       "4  [, , regret, see, , , since, rating, , , imdb,...   \n",
+       "                                            filtered  \\\n",
+       "0  [dont, look, , basement, , , easy, , knock, tr...   \n",
+       "1  [flat, spoilers, five, med, students, nelson, ...   \n",
+       "2  [, may, seem, far, fetched, really, , real, li...   \n",
+       "3  [grandpas, hourmore, gangsters, , detailed, de...   \n",
+       "4  [, regret, seen, , since, ratings, , imdb, rel...   \n",
        "\n",
-       "                                         rawFeatures   \n",
-       "0  (63.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0,...  \\\n",
-       "1  (67.0, 2.0, 0.0, 1.0, 2.0, 0.0, 1.0, 0.0, 2.0,...   \n",
-       "2  (69.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,...   \n",
-       "3  (36.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0,...   \n",
-       "4  (75.0, 2.0, 2.0, 1.0, 1.0, 0.0, 2.0, 0.0, 0.0,...   \n",
+       "                                         rawFeatures  \\\n",
+       "0  (33.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0,...   \n",
+       "1  (35.0, 1.0, 0.0, 1.0, 1.0, 0.0, 2.0, 1.0, 0.0,...   \n",
+       "2  (38.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0,...   \n",
+       "3  (19.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0,...   \n",
+       "4  (42.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
        "\n",
        "                                         featuresIDF  \n",
-       "0  (0.012408902935529893, 0.0, 0.5369306891672687...  \n",
-       "1  (0.01319676978857941, 0.8910976455736771, 0.0,...  \n",
-       "2  (0.013590703215104168, 0.0, 0.0, 0.0, 0.0, 0.6...  \n",
-       "3  (0.007090801677445653, 0.0, 0.5369306891672687...  \n",
-       "4  (0.014772503494678443, 0.8910976455736771, 1.0...  "
+       "0  (0.006499901537658516, 0.0, 0.0, 0.59092304052...  \n",
+       "1  (0.006893834964183274, 0.5042821683885013, 0.0...  \n",
+       "2  (0.0074847351039704114, 0.0, 0.0, 0.0, 0.79757...  \n",
+       "3  (0.0037423675519852057, 0.0, 0.614309607359265...  \n",
+       "4  (0.008272601957019928, 0.0, 0.6143096073592659...  "
       ]
      },
-     "execution_count": 40,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -795,19 +742,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Tokenizer_d41190dfd02b,\n",
-       " StopWordsRemover_42b3525e1430,\n",
-       " CountVectorizerModel: uid=CountVectorizer_a1a420fd1f66, vocabularySize=1000,\n",
-       " IDFModel: uid=IDF_fb6e03ab72e7, numDocs=10154, numFeatures=1000]"
+       "[Tokenizer_fd2fbe97cf6a,\n",
+       " StopWordsRemover_b28a7aead20e,\n",
+       " CountVectorizerModel: uid=CountVectorizer_ced36f4efa0e, vocabularySize=1000,\n",
+       " IDFModel: uid=IDF_515eaa831f6e, numDocs=10154, numFeatures=1000]"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -819,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -829,25 +776,25 @@
        " 'movie',\n",
        " 'film',\n",
        " 'one',\n",
-       " 'see',\n",
-       " 'make',\n",
        " 'like',\n",
        " 'good',\n",
-       " 'get',\n",
-       " 'well',\n",
-       " 'time',\n",
-       " 'character',\n",
-       " 'watch',\n",
-       " 'bad',\n",
        " 'even',\n",
-       " 'story',\n",
+       " 'time',\n",
        " 'really',\n",
-       " 'think',\n",
-       " 'show',\n",
-       " 'scene']"
+       " 'see',\n",
+       " 'story',\n",
+       " 'much',\n",
+       " 'well',\n",
+       " 'get',\n",
+       " 'bad',\n",
+       " 'great',\n",
+       " 'also',\n",
+       " 'people',\n",
+       " 'dont',\n",
+       " 'first']"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -861,12 +808,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Metics for the model"
+    "# Metrics for the model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -901,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -909,7 +856,7 @@
      "output_type": "stream",
      "text": [
       "Training started.\n",
-      "Model created in 1.11s.\n"
+      "Model created in 2.26s.\n"
      ]
     },
     {
@@ -924,11 +871,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Precision = 0.8729 Recall = 0.8356 F1 Score = 0.8539\n",
+      "Precision = 0.8745 Recall = 0.8247 F1 Score = 0.8489\n",
       "Confusion matrix \n",
-      " [[1000  162]\n",
-      " [ 219 1113]]\n",
-      "Total time 7.30s.\n"
+      " [[ 982  160]\n",
+      " [ 237 1115]]\n",
+      "Total time 10.67s.\n"
      ]
     }
    ],
@@ -954,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -962,12 +909,12 @@
      "output_type": "stream",
      "text": [
       "Training started.\n",
-      "Model created in 4.98s.\n",
-      "Precision = 0.8557 Recall = 0.6967 F1 Score = 0.7680\n",
+      "Model created in 8.75s.\n",
+      "Precision = 0.8722 Recall = 0.6941 F1 Score = 0.7730\n",
       "Confusion matrix \n",
-      " [[ 744  184]\n",
-      " [ 475 1091]]\n",
-      "Total time 11.64s.\n"
+      " [[ 729  163]\n",
+      " [ 490 1112]]\n",
+      "Total time 17.00s.\n"
      ]
     }
    ],
@@ -992,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1000,12 +947,12 @@
      "output_type": "stream",
      "text": [
       "Training started.\n",
-      "Model created in 1.21s.\n",
-      "Precision = 0.8800 Recall = 0.8317 F1 Score = 0.8552\n",
+      "Model created in 2.31s.\n",
+      "Precision = 0.8729 Recall = 0.8238 F1 Score = 0.8477\n",
       "Confusion matrix \n",
-      " [[ 992  153]\n",
-      " [ 227 1122]]\n",
-      "Total time 7.71s.\n"
+      " [[ 981  162]\n",
+      " [ 238 1113]]\n",
+      "Total time 10.68s.\n"
      ]
     }
    ],
@@ -1030,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -1038,12 +985,12 @@
      "output_type": "stream",
      "text": [
       "Training started.\n",
-      "Model created in 1.93s.\n",
-      "Precision = 0.8682 Recall = 0.8330 F1 Score = 0.8502\n",
+      "Model created in 3.78s.\n",
+      "Precision = 0.8698 Recall = 0.8307 F1 Score = 0.8498\n",
       "Confusion matrix \n",
-      " [[ 997  168]\n",
-      " [ 222 1107]]\n",
-      "Total time 8.31s.\n"
+      " [[ 993  166]\n",
+      " [ 226 1109]]\n",
+      "Total time 12.66s.\n"
      ]
     }
    ],
@@ -1082,7 +1029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1110,14 +1057,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['movie', 'make', 'like', 'good', 'get', 'watch', 'bad', 'even', 'story', 'great', 'look', 'say', 'also', 'play', 'love', 'thing', 'seem', 'life', 'want', 'plot', 'try', 'year', 'act', 'still', 'something', 'guy', 'performance', 'nothing', 'actually', 'young', 'role', 'become', 'point', 'minute', 'pretty', 'world', 'kill', 'horror', 'mean', 're', 'script', 'whole', 'least', 'may', 'acting', 'always', 'enjoy', 'family', 'live', 'series', 'anything', 'reason', 'effect', 'idea', 'fun', 'especially', 'bring', 'maybe', 'different', 'money', 'someone', 'job', 'true', 'shoot', 'waste', 'recommend', 'instead', 'hour', 'excellent', 'short', 'beautiful', 'else', 'war', 'view', 'half', 'attempt', 'poor', 'suppose', 'classic', 'human', 'stupid', 'rest', 'lack', 'either', 'completely', 'meet', 'wrong', 'dialogue', 'save', 'joke', 'awful', 'perfect', 'definitely', 'flick', 'terrible', 'fine', 'wonder', 'wonderful', 'sit', 'low', 'guess', 'experience', 'spend', 'fail', 'throw', 'win', 'relationship', 'cut', 'boring', 'favorite', 'horrible', 'rent', 'support', 'strong', 'amazing', 'heart', 'today', 'ill', 'brilliant', 'complete', 'chance', 'unfortunately', 'decent', 'simple', 'obviously', 'highly', 'silly', 'hilarious', 'crap', 'cheap', 'capture', 'god', 'dialog', 'seriously', 'none', 'zombie', 'apparently', 'ridiculous', 'annoying', 'bore', 'touch', 'avoid', 'modern', 'enjoyable', 'predictable', 'discover', 'deep', 'romantic', 'emotion', 'suck', 'bunch', 'dull', 'oscar', 'entertaining', 'mess', 'fantastic', 'premise', 'realistic', 'sorry', 'lame', 'accent', 'bother', 'dumb', 'masterpiece', 'appreciate', 'memorable', 'beauty', 'atmosphere', 'perfectly', 'unless', 'poorly', 'spoiler', 'superb', 'portrayal', 'personal', 'powerful', 'okay', 'otherwise', 'scream', 'badly', 'share', 'unique', 'fake', 'society', 'era', 'hole', 'awesome', 'flat', 'pathetic', 'plain', 'trash', 'pointless', 'rip', 'solid', 'excuse', 'complex', 'terrific', 'incredible', 'natural', 'garbage']\n"
+      "['movie', 'like', 'even', 'story', 'well', 'get', 'bad', 'great', 'also', 'dont', 'make', 'acting', 'love', 'plot', 'best', 'life', 'better', 'still', 'say', 'something', 'watching', 'thing', 'doesnt', 'nothing', 'didnt', 'actually', 'years', 'cant', 'thats', 'want', 'pretty', 'young', 'world', 'horror', 'whole', 'least', 'isnt', 'may', 'theres', 'always', 'guy', 'minutes', 'series', 'point', 'anything', 'script', 'performance', 'worst', 'family', 'played', 'trying', 'fun', 'especially', 'maybe', 'different', 'shows', 'day', 'money', 'looks', 'true', 'reason', 'someone', 'wasnt', 'plays', 'effects', 'job', 'instead', 'excellent', 'idea', 'beautiful', 'half', 'else', 'poor', 'war', 'either', 'stupid', 'performances', 'completely', 'rest', 'recommend', 'boring', 'wrong', 'mean', 'classic', 'couldnt', 'awful', 'definitely', 'terrible', 'dialogue', 'perfect', 'gives', 'human', 'wonderful', 'supposed', 'become', 'waste', 'problem', 'worse', 'seemed', 'liked', 'loved', 'care', 'horrible', 'guess', 'fine', 'flick', 'amazing', 'killer', 'low', 'lives', 'kill', 'enjoyed', 'works', 'ill', 'brilliant', 'favorite', 'unfortunately', 'wouldnt', 'decent', 'hour', 'obviously', 'heart', 'save', 'strong', 'highly', 'experience', 'silly', 'wonder', 'looked', 'simple', 'attempt', 'complete', 'annoying', 'hilarious', 'saying', 'crap', 'relationship', 'seriously', 'none', 'apparently', 'cheap', 'ridiculous', 'today', 'modern', 'finds', 'enjoyable', 'predictable', 'tells', 'supporting', 'ways', 'romantic', 'tried', 'arent', 'bunch', 'dull', 'fantastic', 'brought', 'realistic', 'moving', 'sorry', 'write', 'avoid', 'killing', 'greatest', 'rent', 'lame', 'memorable', 'oscar', 'effort', 'atmosphere', 'perfectly', 'joke', 'beauty', 'masterpiece', 'unless', 'poorly', 'superb', 'powerful', 'personal', 'dumb', 'okay', 'mess', 'otherwise', 'fails', 'brings', 'spent', 'wasted', 'badly', 'unique', 'bored', 'portrayal', 'society', 'era', 'awesome', 'pathetic', 'flat', 'plain', 'pointless', 'fake', 'recommended']\n"
      ]
     }
    ],
@@ -1138,7 +1085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1158,7 +1105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1166,12 +1113,12 @@
      "output_type": "stream",
      "text": [
       "Training started.\n",
-      "Model created in 0.94s.\n",
-      "Precision = 0.8659 Recall = 0.8124 F1 Score = 0.8383\n",
+      "Model created in 2.14s.\n",
+      "Precision = 0.8549 Recall = 0.8165 F1 Score = 0.8352\n",
       "Confusion matrix \n",
-      " [[ 964  171]\n",
-      " [ 255 1104]]\n",
-      "Total time 6.05s.\n"
+      " [[ 974  185]\n",
+      " [ 245 1090]]\n",
+      "Total time 10.91s.\n"
      ]
     }
    ],
@@ -1186,6 +1133,42 @@
     "print(f\"Total time {time.time()-start:.2f}s.\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training started.\n",
+      "Model created in 1.63s.\n",
+      "Precision = 0.8627 Recall = 0.8172 F1 Score = 0.8394\n",
+      "Confusion matrix \n",
+      " [[ 973  175]\n",
+      " [ 246 1100]]\n",
+      "Total time 9.96s.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyspark.ml.classification import MultilayerPerceptronClassifier\n",
+    "# Multilayer Perceptron Classifier for a classification task with 1000 input features, a hidden layer with 30 nodes, and 2 output classes\n",
+    "# The input layer must match the dimensionality of the input data currently = 1000\n",
+    "layers = [200, 8, 2]\n",
+    "\n",
+    "# create the trainer and set its parameters\n",
+    "classifier = MultilayerPerceptronClassifier(maxIter=10, layers=layers,featuresCol = \"features\", blockSize=128, seed=1234)\n",
+    "pipeline = Pipeline(stages=[classifier])\n",
+    "start = time.time()\n",
+    "print(f\"Training started.\")\n",
+    "model = pipeline.fit(transformed_data)\n",
+    "print(f\"Model created in {time.time()-start:.2f}s.\")\n",
+    "m_metrics_l(model,transformed_test)\n",
+    "print(f\"Total time {time.time()-start:.2f}s.\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,