Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents

With the growth of social media, document sentiment classification has become an active area of research in this decade. It can be viewed as a special case of topical classification applied only to subjective portions of a document (sources of sentiment). Hence, the key task in document sentiment cl...

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Bibliographic Details
Published in:Information retrieval (Boston) 2011-06, Vol.14 (3), p.337-353
Main Authors: Sarvabhotla, Kiran, Pingali, Prasad, Varma, Vasudeva
Format: Article
Language:English
Subjects:
Classification
Computer Science
Data mining
Data Mining and Knowledge Discovery
Data Structures and Information Theory
Documents
Feature extraction
Feature selection
Information retrieval
Information Storage and Retrieval
Language
Linguistics
Motion pictures
Natural Language Processing (NLP)
Pattern Recognition
Sentiment analysis
Social networks
Studies
Subjectivity
User generated content
Web Mining for Search
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Summary:With the growth of social media, document sentiment classification has become an active area of research in this decade. It can be viewed as a special case of topical classification applied only to subjective portions of a document (sources of sentiment). Hence, the key task in document sentiment classification is extracting subjectivity. Existing approaches to extract subjectivity rely heavily on linguistic resources such as sentiment lexicons and complex supervised patterns based on part-of-speech (POS) information. This makes the task of subjective feature extraction complex and resource dependent. In this work, we try to minimize the dependency on linguistic resources in sentiment classification. We propose a simple and statistical methodology called review summary (RSUMM) and use it in combination with well-known feature selection methods to extract subjectivity. Our experimental results on a movie review dataset prove the effectiveness of the proposed methodology.
ISSN:1386-4564
1573-7659
DOI:10.1007/s10791-010-9161-5