Feature-Opinion Extraction using Formal and Informal Classifier Technique

Abstract

Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. This is not only true for organizations but also true for individuals. Our beliefs and perceptions of reality, and the choices we make, are, to a considerable degree, conditioned upon how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. Unfortunately, reading through all customer reviews is difficult, especially for popular items, the number of reviews can be up to hundreds or even thousands. This makes it difficult for a potential customer to read them and make an informed decision. Thus a compressed and precise opinion list is what a user would generally desire. The contents available on the Web are not in the standard format. The idea is to classify these contents as formal and informal. The type of algorithm used is linguistic. It incorporates grammatical and other knowledge of the language which helps in understanding the text, thus trying to improve the mining approach to mine product features and their opinions from Web opinion sources for formal as well as for informal text.