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sentiment-analysis.html
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<!DOCTYPE html>
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<meta name="author" content="Srijan Mishra"/>
<meta name="description" content="Sentiment Analysis project details."/>
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<title>Srijan Mishra</title>
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<li><a href="gsoc.html">Google Summer of Code 2015</a></li>
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<h3 class="zero-top"><strong>Sentiment Analysis</strong></h3>
<p>
The was aimed to predict the polarity (positive/negative) of a given text data using machine
learning and natural language techniques. The sentiment analysis was applied to a movie review
dataset.
</p>
<h4><strong>Implementation Details</strong></h4>
<p>
<strong>Dataset Size:</strong> 2000 records (1000 of each polarity - positive and negative)<br>
<strong>Testing and Cross validation:</strong> 10-30% of the entire dataset
</p>
<h5><strong>Language Model</strong></h5>
<p>
Bigram Frequencies, takes in account only adjacent pair of words. Relationship between words further
apart in the sentence is unaccounted for in this model.
</p>
<h5><strong>Classifiers</strong> (implemented using scikit-learn)</h5>
<ul>
<li>Multinomial Naive Bayesian</li>
<li>Stochastic Gradient Descent</li>
</ul>
<h5><strong>Results</strong></h5>
<p>
The following graphs show mean accuracy, F1 score of positive and negative classes vs training set
size.
</p>
<img class="img-responsive graph" src="img/mnb.jpeg"> <br>
<img class="img-responsive graph" src="img/sgd.jpeg">
<h5><strong>Links</strong></h5>
<ul>
<li><a href="https://github.com/codebuff/sentiment-analysis" target="_blank">Project link</a> on
github.com
</li>
<li><a href="http://scikit-learn.org/" target="_blank">scikit-learn</a> on scikit-learn.org/</li>
</ul>
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