Abstract
The feature selection or extraction is the most important task in Opinion mining and Sentimental Analysis
(OSMA) for calculating the polarity score. These scores are used to determine the positive, negative, and
neutral polarity about the product, user reviews, user comments, and etc., in social media for the purpose
of decision making and Business Intelligence to individuals or organizations. In this paper, we have
performed an experimental study for different feature extraction or selection techniques available for
opinion mining task. This experimental study is carried out in four stages. First, the data collection process
has been done from readily available sources. Second, the pre-processing techniques are applied
automatically using the tools to extract the terms, POS (Parts-of-Speech). Third, different feature selection
or extraction techniques are applied over the content. Finally, the empirical study is carried out for
analyzing the sentiment polarity with different features.
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