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Approaches to Sentiment Analysis on Product Reviews
Abstract
Purchase decisions are better when opinions/reviews about products are considered. Similarly, reviewing customer feedback help in improving the sale and ultimately benefit the business. Web 2.0 provides various platforms such as Twitter, Facebook, etc. where one can comment, review, or post to express his/her happiness, anger, disbelief, sadness toward products, people, etc. To computationally analyze the sentiments in text requires a better understanding of the technologies used in sentiment analysis. This chapter gives a comprehensive understanding about the techniques used in sentiment analysis. Machine learning approaches are mostly used for sentiment analysis. Whereas, as per the text and required results, lexicon-based approaches are also used for the same purpose. This chapter includes the discussion on the evaluation parameters for the sentiment analysis. This chapter would also highlight ontology approach for sentiment analysis and outstanding contributions made in this field. Keywords: Sentiment Analysis, Product reviews, Supervised learning, Unsupervised learning, Social networking websites, Ontology
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