Positive Negative Neutral Sentiment Analysis Using Dual Sentiment Analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. Ideally, an opinion mining tool would process a set of search results for a given item, generating a list of product attributes (quality, features, etc.) and aggregating opinions about each of them (poor, mixed, good). Then begin by identifying the unique properties of this problem and develop a method for automatically distinguishing between positive and negative reviews. The classifier draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation. Now a days the most popular way to model text in statistical machine learning approaches in sentiment Analysis is Bagof-words (BOW).Determining the polarity of a sentiment bearing expression requires more than a simple bag-of-words approach. Sometimes the performance of BOW remains limited due to some fundamental deficiencies in handling the polarity shift problem. To address this problem for sentiment classification, a model is proposed called dual sentiment analysis (DSA).So that first a novel data expansion technique is proposed by creating a sentiment-reversed review for each training and test review. Basis of this propose a dual training algorithm is proposed to make use of original and reversed training reviews and a dual prediction algorithm is proposed to classify the test reviews by considering two sides of one review. Also extend the DSA framework from polarity (positive-negative) classification to 3-class (positive-negativeneutral) classification finally, for removing DSA’s dependency on an external antonym dictionary for review reversion a corpus-based method is developed to construct a pseudo-antonym dictionary in this way two tasks, nine datasets, two antonym dictionaries, three classification algorithms, and two types of features are considered. At the end results shows the effectiveness of DSA in supervised sentiment classification.