A Novel Lexicalized HMM-Based Learning Framework for Web Opinion Mining
published: Aug. 26, 2009, recorded: June 2009, views: 4433
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !