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A Novel Lexicalized HMM-Based Learning Framework for Web Opinion Mining

A Novel Lexicalized HMM-Based Learning Framework for Web Opinion Mining

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. As e-commerce is becoming more and more popular, the number of customer reviews a product receives grows rapidly. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. In this research, we aim to mine customer reviews of a product and extract highly specific product related entities on which reviewers express their opinions. Opinion expressions and sentences are also identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that have mostly relied on natural language processing techniques or statistic information, we propose a novel machine learning framework using lexicalized HMMs. The approach naturally integrates linguistic features, such as part-of-speech and surrounding contextual clues of words into automatic learning. The experimental results demonstrate the effectiveness of the proposed approach in web opinion mining and extraction from product reviews.


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