Comparative text summarization of product reviews

Abstract

This thesis presents an approach towards summarizing product reviews using comparative sentences by sentiment analysis. Specifically, we consider the problem of extracting and scoring features from natural language text for qualitative reviews in a particular domain. When shopping for a product, customers do not find sufficient time to learn about all products on the market. Similarly, manufacturers do not have proper written sources from which to learn about customer opinions. The only available techniques involve gathering customer opinions, often in text form, from e-commerce and social networking web sites and analyzing them, which is a costly and time-consuming process. In this work I address these issues by applying sentiment analysis, an automated method of finding the opinion stated by an author about some entity in a text document. Here I first gather information about smart phones from many e-commerce web sites. I then present a method to differentiate comparative sentences from normal sentences, form feature sets for each domain, and assign a numerical score to each feature of a product and a weight coefficient obtained by statistical machine learning, to be used as a weight for that feature in ranking various products by linear combinations of their weighted feature scores. In this thesis I also explain what role comparative sentences play in summarizing the product. In order to find the polarity of each feature a statistical algorithm is defined using a small-to-medium sized data set. Then I present my experimental environment and results, and conclude with a review of claims and hypotheses stated at the outset. The approach specified in this thesis is evaluated using manual annotated trained data and also using data from domain experts. I also demonstrate empirically how different algorithms on this summarization can be derived from the technique provided by an annotator. Finally, I review diversified options for customers such as providing alternate products for each feature, top features of a product, and overall rankings for products.