Accurate Sentiment Analysis using Enhanced Machine Learning Models
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

M.Tech / M.E / PhD Thesis | Computer Science & Engineering | India | Volume 4 Issue 9, September 2015

Accurate Sentiment Analysis using Enhanced Machine Learning Models

Rincy Jose, Varghese S Chooralil

Sentiment analysis is the computational study of opinions, sentiments, subjectivity, evaluations, attitudes, views and emotions expressed in text. Sentiment analysis is mainly used to classify the reviews as positive or negative or neutral with respect to a query term. This is useful for consumers who want to analyse the sentiment of products before purchase, or viewers who want to know the public sentiment about a new released movie. Here i present the results of machine learning algorithms for classifying the sentiment of movie reviews which uses a chi-squared feature selection mechanism for training. I show that machine learning algorithms such as Naive Bayes and Maximum Entropy can achieve competitive accuracy when trained using features and the publicly available dataset. It analyse accuracy, precision and recall of machine learning classification mechanisms with chi-squared feature selection technique and plot the relationship between number of features and accuracy using Naive Bayes and Maximum Entropy models. Our method also uses a negation handling as a pre-processing step in order to achieve high accuracy.

Keywords: Sentiment Classification, Negation Handling, sentiment Analysis, Feature Selection

Edition: Volume 4 Issue 9, September 2015

Pages: 252 - 254

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How to Cite this Article?

Rincy Jose, Varghese S Chooralil, "Accurate Sentiment Analysis using Enhanced Machine Learning Models", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SUB157922, Volume 4 Issue 9, September 2015, 252 - 254

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