Analysis on automatic opinion extraction from product reviews

Abstract

Opinion mining also known as sentiment analysis is the computational study of subjective information towards different entities. Entities usually refer to products, organizations, services or/and their features, functions, components and attributes. Opinion mining is a major task of Natural Language Processing (NLP) that studies methods for identifying and extracting opinions from written text, such as product reviews, discussion groups, forums and blogs. Natural Language Processing techniques and lexicon-based approaches for opinion mining are used to extract aspects and customer opinions. Extracting opinion words and product features is an important task in many sentiment analysis applications. Opinion lexicon also plays a very important role because it is very useful for a wide range of tasks. Although there are several opinion lexicons available, it is hard to maintain a universal opinion lexicon to cover all domains. So, it is necessary to expand a known opinion lexicon that is useful for some domains. The aim of this system is to automatically expand opinion lexicon and to extract product features based on the dependency relations. Stanford Core NLP dependency parser is used to identify the dependency relations between features and opinions. Extraction rules are predefined according to these dependency relations. This work proposed an algorithm based on double Propagation to extract feature and opinions. The polarity orientation is annotated by using Vader lexicon. Unlike the existing approaches, this system contributes verbs opinions and verb product features. In order to increase the precision and recall, the system also proposes additional patterns besides 8 rules in Double Propagation. And, general words that are not features and adjectives that are not opinions are filtered in the proposed system. According to experimental studies, our approach is better than the existing state of the art approach.