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Merge branch 'master' of github.com:cathywu/Sentiment-Analysis
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‎egpaper_final.aux

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\@writefile{toc}{\contentsline {subsection}{\numberline {6.9}\hskip -1em.\nobreakspace {}Majority Voting}{4}}
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\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces 3-fold cross validation results on movie dataset. Values repesent positive, negative, or overall accuracy.}}{6}}
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\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Test results on Yelp dataset with Naive Bayes classifier. Values repesent percent of reviews classified as positive for a given star rating.}}{7}}
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\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Test results on Yelp dataset with Maximum Entropy classifier. Values repesent percent of reviews classified as positive for a given star rating.}}{7}}

‎egpaper_final.bbl

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\begin{thebibliography}{1}\itemsep=-1pt
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\bibitem{Martin}
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J.~M. D.~Martin, C.~Fowlkes.
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\newblock Learning to detect natural image boundaries using local brightness,
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color, and texture cues.
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\newblock {\em IEEE Transactions on Pattern Analysis and Machine Intelligence},
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26(5):530--549, 2004.
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‎egpaper_final.blg

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‎egpaper_final.tex

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‎egpaper_final.tex~

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%%%%%%%%% TITLE
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\title{Sentiment Classification using Machine Learning Techniques}
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\author{Pranjal Vashaspati\\
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Institution1\\
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\author{Pranjal Vachaspati\\
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Cathy Wu\\
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We implement a series of classifiers (Naive Bayes, Maximum Entropy, and SVM) to distinguish positive and negative sentiment in critic and user reviews. We apply various processing methods, including negation tagging, part-of-speech tagging, and position tagging to achieve maximum accuracy. We test our classifiers on an external dataset to see how well they generalize. Finally, we use a majority-voting technique to combine classifiers and achieve accuracy of close to 90\% in 3-fold cross-validation.
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We implement a series of classifiers (Naive Bayes, Maximum Entropy, and SVM) to distinguish positive and negative sentiment in critic and user reviews. We apply various processing methods, including negation tagging, part-of-speech tagging, and position tagging to achieve maximum accuracy. We test our classifiers on an external dataset to see how well they generalize. Finally, we use a majority-voting technique to combine classifiers and achieve accuracy of close to 90\% in 3-fold cross-validation\cite{Martin}.
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