A Novel Machine Learning System for Sentiment Analysis and Extraction

Abstract

The huge volume of online reviews makes it difficult for a human to process and extract all significant information to make decisions. As a result, there has been a trend to develop systems that can automatically summarize opinions from a set of reviews. In this respect, the automatic classification and information extraction from users' comments, also known as sentiment analysis (SA) becomes vital to offer users the best responses to users' queries, based on their preferences. In this paper, a novel system hat offers personalized user experiences and solves the semantic-pragmatic gap was presented. Having a system for forecasting sentiments might allow us, to extract opinions from the internet and predict online user's favorites, which could determine valuable for commercial or marketing research. The data used belongs to the tagged corpus positive and negative processed movie reviews introduced by Pang and Lee[1]. The results show that even when a small sample is used, sentiment analysis can be done with high accuracy if appropriate natural language processing algorithms applied.