International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 134

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


Abstract: 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), Volume 4 Issue 9, September 2015, pp. 252-254, https://www.ijsr.net/get_abstract.php?paper_id=SUB157922

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