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dc.contributor.advisor Jacob, Elin K. en Yu, Ning en 2011-10-19T20:18:12Z en 2028-06-19T20:18:13Z en 2012-03-12T00:06:08Z 2011-10-19T20:18:12Z en 2011 en
dc.identifier.uri en
dc.description Thesis (Ph.D.) - Indiana University, Information Science, 2011 en
dc.description.abstract Opinions published on the World Wide Web (Web) offer opportunities for detecting personal attitudes regarding topics, products, and services. The opinion detection literature indicates that both a large body of opinions and a wide variety of opinion features are essential for capturing subtle opinion information. Although a large amount of opinion-labeled data is preferable for opinion detection systems, opinion-labeled data is often limited, especially at sub-document levels, and manual annotation is tedious, expensive and error-prone. This shortage of opinion-labeled data is less challenging in some domains (e.g., movie reviews) than in others (e.g., blog posts). While a simple method for improving accuracy in challenging domains is to borrow opinion-labeled data from a non-target data domain, this approach often fails because of the domain transfer problem: Opinion detection strategies designed for one data domain generally do not perform well in another domain. However, while it is difficult to obtain opinion-labeled data, unlabeled user-generated opinion data are readily available. Semi-supervised learning (SSL) requires only limited labeled data to automatically label unlabeled data and has achieved promising results in various natural language processing (NLP) tasks, including traditional topic classification; but SSL has been applied in only a few opinion detection studies. This study investigates application of four different SSL algorithms in three types of Web content: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. SSL algorithms are also evaluated for their effectiveness in sparse data situations and domain adaptation. Research findings suggest that, when there is limited labeled data, SSL is a promising approach for opinion detection in Web content. Although the contributions of SSL varied across data domains, significant improvement was demonstrated for the most challenging data domain--the blogosphere--when a domain transfer-based SSL strategy was implemented. en
dc.language.iso en en
dc.publisher [Bloomington, Ind.] : Indiana University en
dc.rights This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) license. en
dc.rights.uri en
dc.subject Semi-Supervised Learning en
dc.subject Sentiment Analysis en
dc.subject Domain Transfer en
dc.subject Opinion Detection en
dc.subject blog en
dc.subject text mining en
dc.subject Co-Training en
dc.subject Self-Training en
dc.subject.classification Information Science en
dc.subject.classification Information Technology en
dc.subject.classification Computer Science en
dc.title Semi-Supervised Learning For Identifying Opinions In Web Content en
dc.type Doctoral Dissertation en

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