DUAL SENTIMENT ANALYSIS

Abstract

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 Bag-of-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 somefundamental deficiencies in handling the polarity shift problem. To address this problem for sentiment classification, a model is proposed called dual sentiment analysis (DSA).