Sentiment Analyis of Indian Movie Review with
Various Feature Selection Techniques
Sentiment analysis and opinion mining is an
emerging area of research for analysing web data and capturing
the sentiment of the users. This research presents sentiments
analysis on Indian movie review corpus using machine learning
classifier. Bayesian Classifier has been used in this study for
testing feature selection mechanics. This classifier is trained on
the words/features of the corpus extracted using five feature
selection algorithms (Chi-square, Info-gain, Gain-Ratio, One-R
and relief attribute) and a comparative study have been
performed amongst them. The classifier and feature selection
approaches were evaluated by two different metrics (F-Value,
and False Positive). Results of this study show that: for maximum
number of features, Relief -F feature selection approach is found
to be good with better F-value, Low FP Rate. In addition for the
less number of features, One-R was better than Relief-F. This
electronic document is a “live” template and already defines the
components of your paper [title, text, heads, etc.] in its style sheet.
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approach is found
to be good with better F-value, Low FP Rate. In addition for the
less number of features, One-R was better than Relief-F. This
electronic document is a “live” template and already defines the
components of your paper [title, text, heads, etc.] in its style sheet.</p>
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This product was added to our catalog on Thursday 07 June, 2018.