Sentiment Analysis of Products Using Web

A variety of general rule-based and machine learning techniques are described, which provides some background information on the key underlying NLP processes required, and focus specifically on some of the major problems and solutions, such as detection of sarcasm, use of informal language, spam opinion detection, trustworthiness of opinion holders, and so on.

Abstract

Sentiment analysis determines the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation, affective state or the intended emotional communication. It is a common practice that merchants selling products on the Web ask their customers to review the products and associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This paper describes the concepts of sentiment analysis from unstructured text, looking at why they is useful and what tools and techniques are available. We will describe a variety of general rule-based and machine learning techniques, which provides some background information on the key underlying NLP processes required, and focus specifically on some of the major problems and solutions, such as detection of sarcasm, use of informal language, spam opinion detection, trustworthiness of opinion holders, and so on. We will also discuss problems associated with opinion detection in social media such as blogs, forum posts, twitter, etc. The techniques will be demonstrated with key open-source tools and applications.