OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction

author: Rohini K Srihari, University at Buffalo
published: Sept. 14, 2009,   recorded: July 2009,   views: 5462
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Description

Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. 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 to make an informed decision. The OpinionMiner system designed in this work aims to mine customer reviews of a product and extract high detailed product entities on which reviewers express their opinions. Opinion expressions are identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that employed rule-based or statistical techniques, we propose a novel machine learning approach built under the framework of lexicalized HMMs. The approach naturally integrates multiple important linguistic features into automatic learning. In this paper, we describe the architecture and main components of the system. The evaluation of the proposed method is presented based on processing the online product reviews from Amazon and other publicly available datasets.

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Reviews and comments:

Comment1 Shivaprasad, January 18, 2012 at 10:15 a.m.:

Dear madam
It was nice to listen your lecture and we gathered aome knowledge related to opinion mining and we r still in need of basic knowledge of opinion mining can u pls mail me about this topic

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