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<TITLE>Sentiment Classification using Machine Learning Techniques</TITLE>
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<B> Next:</B> <A NAME="tex2html7"
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<H1 ALIGN=CENTER>Sentiment Classification using Machine Learning Techniques</H1>
<P ALIGN=CENTER><STRONG>Pranjal Vachaspati</STRONG>
<BR><I><TT><SMALL CLASS="SMALL">pranjal@mit.edu</SMALL></TT></I>
</P><P ALIGN=CENTER><STRONG>Cathy Wu</STRONG>
<BR><I><TT><SMALL CLASS="SMALL">cathywu@mit.edu</SMALL></TT> </I>
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<H3>Abstract:</H3>
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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, far outperforming Pang's 2002 work [<A
HREF="node20.html#Pang">7</A>].
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<LI><A NAME="tex2html8"
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<LI><A NAME="tex2html9"
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<LI><A NAME="tex2html10"
HREF="node3.html">The User Review Domain</A>
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<LI><A NAME="tex2html12"
HREF="node5.html">The Naive Bayes Classifier</A>
<LI><A NAME="tex2html13"
HREF="node6.html">The Maximum Entropy Classifier</A>
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<LI><A NAME="tex2html15"
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<LI><A NAME="tex2html17"
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<LI><A NAME="tex2html18"
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<LI><A NAME="tex2html19"
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<LI><A NAME="tex2html20"
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<LI><A NAME="tex2html21"
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<LI><A NAME="tex2html22"
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<LI><A NAME="tex2html23"
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<LI><A NAME="tex2html24"
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<LI><A NAME="tex2html25"
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<LI><A NAME="tex2html28"
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<ADDRESS>
Pranjal Vachaspati
2012-02-05
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