Classification Approach and Analysis for Predicting Social Emotion on User Review
Bhushan R. Chincholkar · International Journal for Research in Applied Science and Engineering Technology · 2021
Classification of opinions or sentiments is the core task in opinion mining. To accomplish this task, often Bag-Of-Words (BOW) is used as a feature for training a classifier in statistical machine learning.(ppr3) BOW (Bag-of-Words) is the most common machine learning method used for sentiment classification. However, this model does not address the problem of polarity shift due to fundamental limitations. This is one of the reasons which affect its overall accuracy. Based on the review of current prediction algorithms of network security situation, prediction algorithms. This paper discusses a method using dual training and dual prediction for sentiment classification while addressing the problem of polarity shift and also evaluates it. We propose a modified fuzzy approach with two stage training for dealing with text ambiguity and classifying types of hate positive, negative and neutral speech and compare its performance with those popular methods as well as some existing fuzzy approaches, while the features are prepared through the bag-of-words and word embedding feature extraction methods alongside the correlation based feature subset selection method. The experimental results show that the proposed fuzzy method outperforms the other methods in most cases. It extends the given framework from polarity (positive-negative) classification to 3-class (positive negative-neutral) classification, by taking the all reviews into consideration.