An Experimental Study of Feature Extraction Techniques in Opinion Mining

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.