Dual Sentiment Analysis with Three-Stage Model for Complex Polarity Shift Patterns with Two Sides of One Review
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2017, Vol 4, Issue 8
Abstract
Sentiment classification is a unique process of text categorization whose objective is to categorize a text related to the sentimental polarities of opinions it consists of constructive or adverse, positive or negative. Bag-of-words (BOW) is now the major famous method to form text in numerical machine learning methods in Sentiment Analysis (SA). On the other hand, the accuracy of BOW sometimes still remains lesser because of various basic disadvantages in handling the polarity shift problem. To deal with this problem, Dual Sentiment Analysis (DSA) is proposed in the recent work and it is used for SA classification. However in DSA is should consider more difficult polarity shift patterns are middle, subjunctive and sentiment-inconsistent sentences in forming reversed reviews. To solve this problem a Three-Stage Model (TSM) is combined to Dual Sentiment Analysis (DSA) classifier named as DSA-TSM. Initially divide the each set of documents into a set of sub-sentences and build a hybrid classifier which creates the rules and machine learning model to distinguish precise and hidden polarity shifts, correspondingly. Secondly, corpus based method is introduced to build a pseudo-antonym dictionary in the direction of polarity shift elimination technique, to remove polarity shift in negations. Finally, dual training and dual prediction algorithm is proposed for learning a sentiment classifier which classifiers the polarity into three major classes such as positive-negative-neutral by considering the neutral reviews into consideration. The results of the proposed DSA-TSM model are significantly improved when compared to DSA schema and other methods in terms of accuracy, precision, recall and f-measure
Authors and Affiliations
S. Vetrivel
Keywords
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