| Summary: | Online reviews are immensely valuable for customers to make informed decisions on product purchase, hotel booking, etc., and for businesses to improve the quality of their products and services. However, customer reviews grow very rapidly in quantity, while varying largely in quality. It is practically impossible for users to read through all the reviews for good decision-making.
Opinion mining, also known as sentiment analysis, has been employed to automatically discover and summarize online reviews. In this thesis, we focus on the problem of aspect-based opinion mining of customer reviews. Our goal is to study and develop computational opinion mining techniques to support users to digest the huge amount of review data. In particular, we study three closely related problems as described below.
The first problem deals with extracting aspect terms and opinion words that appear in customer reviews. We propose a generalized corpus statistics association based bootstrapping approach (ABOOT). ABOOT starts with a small list of annotated aspect seeds, and then iteratively extracts a large number of domain-specific aspect terms and opinion words from a given review corpus. ABOOT is able to work properly with only one seed, which can be simply domain word, e.g., "hotel" for hotel reviews.
Our second problem focuses on identifying implicit aspects for the opinion words devoid of explicit aspects. Implicit aspects refer to the aspects that do not appear but are implied by opinion words in reviews. In opinion mining, very little work has been done on this problem. We propose a cooccurrence association rule mining method (coARM). coARM first discovers a significant set of association rules from a review corpus, and then it applies the rules to the opinion words devoid of explicit aspects for implicit aspect identification.
The third part of this thesis deals with modeling customer reviews with aims at identifying semantic aspects and opinions as well as predicting overall review ratings in a unified framework. We introduce a new supervised joint topic model named supervised joint aspect and opinion model (SJAOM). SJAOM incorporates the overall ratings as supervision data, and simultaneously models the pairwise aspect terms and opinion words in each review. One key advantage of SJAOM is its ability to jointly identify the semantic aspects and opinions that are predictive of the overall ratings of reviews.
Experimental results on real-world customer reviews demonstrate the benefits of our proposed methods for opinion mining problems, notably the SJAOM model.
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