Semantic Aspect Based Sentiment Classification using Normalized Yahoo Distance based Particle Swarm Optimization

People’s opinions and experience are important sources of information in our everyday life. In the modern digital age, text is the main method of communicating information on the Internet. Sentiment Classification is the process of judging the sentiments and emotions from reviews. The paper investigates new approaches to the automated extraction of opinions and aspects and sentiment classification from customer reviews. It focuses on aspect-based sentiment classification from customer reviews. Evaluations algorithms and lexicon approaches with normalized yahoo distance are utilized based on semantic relations of customer reviews. The contributions are significant, given both the rapid explosion of today’s accessibility to the Internet and people’s desire to make informed decisions.

Keywords:

Aspects , Sentiments , Sentiment Classification , Part of Speech Tagging , Particle Swarm Optimization.