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  1. +4 −0 egpaper_final.aux
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  3. BIN egpaper_final.pdf
  4. +14 −10 egpaper_final.tex
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@@ -39,4 +39,8 @@
\@writefile{toc}{\contentsline {subsection}{\numberline {3.8}\hskip -1em.\nobreakspace {}Color}{4}}
\bibstyle{ieee}
\bibdata{egbib}
+\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces 3-fold cross validation results on movie dataset. Values repesent positive, negative, or overall accuracy.}}{5}}
\@writefile{toc}{\contentsline {section}{\numberline {4}\hskip -1em.\nobreakspace {}Final copy}{5}}
+\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Test results on Yelp dataset with Naive Bayes classifier. Values repesent percent of reviews classified as positive for a given star rating.}}{6}}
+\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Test results on Yelp dataset with Maximum Entropy classifier. Values repesent percent of reviews classified as positive for a given star rating.}}{6}}
+\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Test results on Yelp dataset with SVM classifier. Values repesent percent of reviews classified as positive for a given star rating.}}{6}}
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@@ -375,11 +375,11 @@ \subsection{Appendix A}
No-negation & Unigrams & 16165 & Frequency & 0.94 & 0.62 & 0.78 & - & - & - & 0.82 & 0.82 & 0.82 \\
No-negation & Unigrams & 16165 & Presence & 0.87 & 0.72 & 0.82 & 0.85 & 0.87 & 0.86 & 0.85 & 0.84 & 0.84 \\
No-negation & Bigrams & 16165 & Frequency & 0.92 & 0.64 & 0.78 & - & - & - & 0.77 & 0.81 & 0.79 \\
-No-negation & Bigrams & 16165 & Presence & 0.89 & 0.73 & 0.81 & 0.79 & 0.82 & 0.81 & 0.8 & 0.81 & 0.8 \\
+No-negation & Bigrams & 16165 & Presence & 0.89 & 0.73 & 0.81 & 0.79 & 0.82 & 0.81 & 0.8 & 0.81 & 0.80 \\
adjectives & Unigrams & 16165 & Frequency & 0.95 & 0.52 & 0.73 & - & - & - & 0.75 & 0.77 & 0.76 \\
default & Bigrams & 2633 & Frequency & 0.91 & 0.46 & 0.69 & - & - & - & 0.74 & 0.75 & 0.75 \\
default & Bigrams & 16165 & Frequency & 0.92 & 0.64 & 0.78 & - & - & - & 0.78 & 0.79 & 0.78 \\
-default & Unigrams & 2633 & Frequency & 0.96 & 0.5 & 0.74 & - & - & - & 0.81 & 0.79 & 0.8 \\
+default & Unigrams & 2633 & Frequency & 0.96 & 0.5 & 0.74 & - & - & - & 0.81 & 0.79 & 0.80 \\
default & Unigrams & 16165 & Frequency & 0.93 & 0.59 & 0.76 & - & - & - & 0.82 & 0.81 & 0.82 \\
default & Unigrams & maximum & Frequency & 0.95 & 0.49 & 0.72 & - & - & - & 0.82 & 0.81 & 0.82 \\
partofspeech & Bigrams & 16165 & Frequency & 0.96 & 0.47 & 0.71 & - & - & - & 0.82 & 0.82 & 0.82 \\
@@ -396,21 +396,22 @@ \subsection{Appendix A}
partofspeech & Bigrams & 16165 & Presence & 0.89 & 0.73 & 0.81 & 0.84 & 0.84 & 0.84 & 0.79 & 0.82 & 0.8 \\
partofspeech & Unigrams & 16165 & Presence & 0.86 & 0.76 & 0.81 & 0.85 & 0.85 & 0.85 & 0.84 & 0.83 & 0.84 \\
position & Bigrams & 16165 & Presence & 0.87 & 0.66 & 0.76 & 0.82 & 0.83 & 0.82 & 0.73 & 0.76 & 0.74 \\
-position & Unigrams & 16165 & Presence & 0.86 & 0.78 & 0.82 & 0.84 & 0.85 & 0.85 & 0.8 & 0.8 & 0.8 \\
-verbs & Unigrams & maximum & Presence & 0.8 & 0.54 & 0.67 & 0.65 & 0.65 & 0.65 & 0.64 & 0.63 & 0.635 \\
-adjectives & Unigrams & 16165 & TF-IDF & 0.82 & 0.6 & 0.71 & - & - & - & 0.79 & 0.76 & 0.77 \\
+position & Unigrams & 16165 & Presence & 0.86 & 0.78 & 0.82 & 0.84 & 0.85 & 0.85 & 0.80 & 0.80 & 0.80 \\
+verbs & Unigrams & maximum & Presence & 0.80 & 0.54 & 0.67 & 0.65 & 0.65 & 0.65 & 0.64 & 0.63 & 0.635 \\
+adjectives & Unigrams & 16165 & TF-IDF & 0.82 & 0.60 & 0.71 & - & - & - & 0.79 & 0.76 & 0.77 \\
default & Bigrams & 2633 & TF-IDF & 0.92 & 0.46 & 0.69 & - & - & - & 0.76 & 0.71 & 0.74 \\
-default & Bigrams & 16165 & TF-IDF & 0.9 & 0.68 & 0.79 & - & - & - & 0.83 & 0.74 & 0.79 \\
-default & Unigrams & 2633 & TF-IDF & 0.85 & 0.52 & 0.74 & - & - & - & 0.81 & 0.79 & 0.8 \\
-default & Unigrams & 16165 & TF-IDF & 0.88 & 0.68 & 0.78 & - & - & - & 0.83 & 0.77 & 0.8 \\
+default & Bigrams & 16165 & TF-IDF & 0.90 & 0.68 & 0.79 & - & - & - & 0.83 & 0.74 & 0.79 \\
+default & Unigrams & 2633 & TF-IDF & 0.85 & 0.52 & 0.74 & - & - & - & 0.81 & 0.79 & 0.80 \\
+default & Unigrams & 16165 & TF-IDF & 0.88 & 0.68 & 0.78 & - & - & - & 0.83 & 0.77 & 0.80 \\
default & Unigrams & maximum & TF-IDF & 0.86 & 0.65 & 0.76 & - & - & - & 0.83 & 0.78 & 0.81 \\
partofspeech & Bigrams & 16165 & TF-IDF & 0.89 & 0.67 & 0.78 & - & - & - & 0.79 & 0.74 & 0.76 \\
partofspeech & Unigrams & 16165 & TF-IDF & 0.89 & 0.63 & 0.76 & - & - & - & 0.81 & 0.78 & 0.79 \\
position & Bigrams & 16165 & TF-IDF & 0.89 & 0.59 & 0.74 & - & - & - & 0.79 & 0.69 & 0.74 \\
position & Unigrams & 16165 & TF-IDF & 0.91 & 0.61 & 0.76 & - & - & - & 0.81 & 0.71 & 0.76 \\
-verbs & Unigrams & maximum & TF-IDF & 0.64 & 0.57 & 0.6 & - & - & - & 0.62 & 0.66 & 0.64 \\
+verbs & Unigrams & maximum & TF-IDF & 0.64 & 0.57 & 0.60 & - & - & - & 0.62 & 0.66 & 0.64 \\
\hline
\end{tabular}
+\caption{3-fold cross validation results on movie dataset. Values repesent positive, negative, or overall accuracy.}
\end{figure*}
%-------------------------------------------------------------------------
@@ -437,6 +438,7 @@ \subsection{Appendix B}
verbs & Unigrams & maximum & Presence & 0.44 & 0.43 & 0.41 & 0.37 & 0.32 & 0.56 \\
\hline
\end{tabular}
+\caption{Test results on Yelp dataset with Naive Bayes classifier. Values repesent percent of reviews classified as positive for a given star rating.}
\end{figure*}
\begin{figure*}
@@ -460,6 +462,7 @@ \subsection{Appendix B}
verbs & Unigrams & maximum & Presence & 0.43 & 0.41 & 0.38 & 0.34 & 0.30 & 0.56 \\
\hline
\end{tabular}
+\caption{Test results on Yelp dataset with Maximum Entropy classifier. Values repesent percent of reviews classified as positive for a given star rating.}
\end{figure*}
\begin{figure*}
@@ -477,12 +480,13 @@ \subsection{Appendix B}
adjectives & Unigrams & 16165 & Presence & 0.71 & 0.71 & 0.61 & 0.46 & 0.37 & 0.67 \\
verbs & Unigrams & 16165 & Presence & 0.45 & 0.45 & 0.42 & 0.38 & 0.32 & 0.57 \\
default & Unigrams & maximum & Presence & - & - & - & - & - & - \\
-position & Unigrams & maximum & Presence & - & - & - & - & - & \\
+position & Unigrams & maximum & Presence & - & - & - & - & - & - \\
partofspeech & Unigrams & maximum & Presence & - & - & - & - & - & - \\
adjectives & Unigrams & maximum & Presence & 0.71 & 0.71 & 0.61 & 0.46 & 0.37 & 0.67 \\
verbs & Unigrams & maximum & Presence & 0.45 & 0.45 & 0.42 & 0.38 & 0.32 & 0.57 \\
\hline
\end{tabular}
+\caption{Test results on Yelp dataset with SVM classifier. Values repesent percent of reviews classified as positive for a given star rating.}
\end{figure*}
%-------------------------------------------------------------------------
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@@ -411,6 +411,7 @@ position & Unigrams & 16165 & TF-IDF & 0.91 & 0.61 & 0.76 & - & - & - & 0.81 & 0
verbs & Unigrams & maximum & TF-IDF & 0.64 & 0.57 & 0.6 & - & - & - & 0.62 & 0.66 & 0.64 \\
\hline
\end{tabular}
+\caption{3-fold cross validation results on movie dataset. Values repesent positive, negative, or overall accuracy.}
\end{figure*}
%-------------------------------------------------------------------------
@@ -437,14 +438,15 @@ adjectives & Unigrams & maximum & Presence & 0.76 & 0.73 & 0.61 & 0.45 & 0.36 &
verbs & Unigrams & maximum & Presence & 0.44 & 0.43 & 0.41 & 0.37 & 0.32 & 0.56 \\
\hline
\end{tabular}
+\caption{Test results on Yelp dataset with Naive Bayes classifier. Values repesent percent of reviews classified as positive for a given star rating.}
\end{figure*}
\begin{figure*}
\begin{tabular}{{|l}*{20}{|c}|r|}
\hline
-\multicolumn{4}{|c|}{Test configurations} & \multicolumn{6}{|c|}{MaxEnt} & \multicolumn{6}{|c|}{SVM}\\
+\multicolumn{4}{|c|}{Test configurations} & \multicolumn{6}{|c|}{MaxEnt}\\
\hline
-Domain & Features & \# of features & Frequency & ***** & **** & *** & ** & * & score & ***** & **** & *** & ** & * & score \\
+Domain & Features & \# of features & Frequency & ***** & **** & *** & ** & * & score \\
\hline
default & Unigrams & 16165 & Frequency & - & - & - & - & - & - \\
default & Unigrams & 16165 & Presence & 0.61 & 0.57 & 0.39 & 0.23 & 0.11 & 0.75 \\
@@ -460,6 +462,7 @@ adjectives & Unigrams & maximum & Presence & 0.75 & 0.72 & 0.62 & 0.45 & 0.3
verbs & Unigrams & maximum & Presence & 0.43 & 0.41 & 0.38 & 0.34 & 0.30 & 0.56 \\
\hline
\end{tabular}
+\caption{Test results on Yelp dataset with Maximum Entropy classifier. Values repesent percent of reviews classified as positive for a given star rating.}
\end{figure*}
\begin{figure*}
@@ -477,12 +480,13 @@ partofspeech & Unigrams & 16165 & Presence & 0.52 & 0.48 & 0.31 & 0.21 & 0.0
adjectives & Unigrams & 16165 & Presence & 0.71 & 0.71 & 0.61 & 0.46 & 0.37 & 0.67 \\
verbs & Unigrams & 16165 & Presence & 0.45 & 0.45 & 0.42 & 0.38 & 0.32 & 0.57 \\
default & Unigrams & maximum & Presence & - & - & - & - & - & - \\
-position & Unigrams & maximum & Presence & - & - & - & - & - & \\
+position & Unigrams & maximum & Presence & - & - & - & - & - & - \\
partofspeech & Unigrams & maximum & Presence & - & - & - & - & - & - \\
adjectives & Unigrams & maximum & Presence & 0.71 & 0.71 & 0.61 & 0.46 & 0.37 & 0.67 \\
verbs & Unigrams & maximum & Presence & 0.45 & 0.45 & 0.42 & 0.38 & 0.32 & 0.57 \\
\hline
\end{tabular}
+\caption{Test results on Yelp dataset with SVM classifier. Values repesent percent of reviews classified as positive for a given star rating.}
\end{figure*}
%-------------------------------------------------------------------------

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