Title

Text classification using semi-supervised approach for multi domain

Document Type

Conference Proceeding

Publisher

Institute of Electrical and Electronics Engineers Inc.

Place of Publication

Piscataway, N.J.

School

School of Science

RAS ID

26592

Comments

Originally published as: Deshmukh, J. S., & Tripathy, A. K. (2017, January). Text classification using semi-supervised approach for multi domain. In Nascent Technologies in Engineering (ICNTE), 2017 International Conference on (pp. 1-5). IEEE. Original article available here

Abstract

The rapid growth of Internet technologies has resulted in an increase in online user's content creation. User generated information, usually represents the people's opinions, thoughts, reviews and sentiments. Automatic sensing and analysis of opinions about products, brands, political publications, etc. is a challenging job. Opinions are expressed in different ways in different domains. Constructing and labeling corpus for every domain is a pricey thing. Words from source and target domains are not always similar; hence classifier trained with one domain to another domain leads into poor performance. Therefore the need of domain adaptation algorithms arises to diminish domain's reliance and tagging costs. Using adapted maximum entropy with bipartite clustering in the proposed work, opinionated words are classified in two categories as positive and negative. Results demonstrated that proposed approach performs fairly well compared to baseline method.

DOI

10.1109/ICNTE.2017.7947982

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