Objective: The goal of this project is to classify social media posts or tweets as positive, negative, or neutral based on the text content using Random Forest Classifier with text processing techniques.
Dataset: Datasets like the Twitter Sentiment Analysis dataset , which includes tweets labeled with sentiment (positive, negative, or neutral).
Steps to Implement:
1. Data Collection:
Obtain a dataset that contains social media posts (e.g., tweets) along with sentiment labels.
2. Data Preprocessing:
Text Cleaning: Remove special characters, URLs, hashtags, mentions, and convert all text to lowercase.
Tokenization: Split the text into individual words (tokens).
Stop Words Removal: Remove common stop words (e.g., ‘the’, ‘is’, ‘and’) that do not contribute much to the sentiment.
Stemming/Lemmatization: Reduce words to their root form (e.g., ‘running’ to ‘run’) using stemming or lemmatization.
3. Feature Extraction:
TF-IDF Vectorization: Convert the text data into numerical format using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization. This step transforms the text into vectors that can be fed into the SVM model.
4. Exploratory Data Analysis (EDA):
Word Cloud: Create word clouds for positive, negative, and neutral tweets to visualize the most common words.
Sentiment Distribution: Analyze the distribution of sentiments across the dataset to understand the class balance.
5. Model Building:
Splitting the Dataset: Split the data into training and testing sets (e.g., 80-20 split).
Model Training: Train a Random Forest model.
Hyperparameter Tuning: Use grid search to optimize hyperparameters like the regularization parameter (C) and the kernel coefficient (gamma) for the SVM.
6. Model Evaluation:
Confusion Matrix: Evaluate the performance of the model using a confusion matrix to understand how well it classifies each sentiment.
Precision, Recall, F1-Score: Calculate these metrics for each class (positive, negative, neutral) to get a comprehensive view of the model’s performance.
Cross-Validation: Use k-fold cross-validation to ensure that the model generalizes well on unseen data.
7. Model Interpretation:
Top Features: Analyze the most important features (words) that contribute to each sentiment class.
Misclassification Analysis: Look into examples where the model misclassifies the sentiment to understand potential improvements.
8. Model Deployment:
Deploy the model using a web framework like Flask, allowing users to input text and get a real-time sentiment prediction.
9. Reporting and Visualization:
Create a dashboard that shows real-time sentiment analysis on new social media posts.
Include visualizations like sentiment trends over time, or the most common positive/negative words.
10. Further Improvements:
Consider experimenting with ensemble models or deep learning techniques like LSTM (Long Short-Term Memory) networks to potentially improve accuracy.