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01.06.2011 | Web Mining for Search

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

verfasst von: Kiran Sarvabhotla, Prasad Pingali, Vasudeva Varma

Erschienen in: Discover Computing | Ausgabe 3/2011

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Abstract

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.

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Positive or negative.

Grading reviews typically use a scale of 1–5, i.e., a starred rating (*).

By sentiment classification we mean document sentiment classification.

A document can be a review or a blog post.

For example, NP, VP, JJ NN, RB JJ not NN, JJ JJ not NN, RB VB, NN JJ not NN, etc., where NN is a noun and RB stands for adverb, JJ stands for adjective, and VB stands for verb.

Support vector machines (SVM), naive Bayes, and maximum entropy-based classification.

The presence of negation, contrast transition, etc.

An annotated collection of documents.

An annotated collection of subjective and objective sentences.

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http://reviews.imdb.com/Reviews.

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http://www.cs.cornell.edu/people/pabo/movie-review-data/rotten_imdb.tar.gz.

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Titel: Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents
verfasst von: Kiran Sarvabhotla
Prasad Pingali
Vasudeva Varma
Publikationsdatum: 01.06.2011
Verlag: Springer Netherlands
Erschienen in: Discover Computing / Ausgabe 3/2011
Print ISSN: 2948-2984
Elektronische ISSN: 2948-2992
DOI: https://doi.org/10.1007/s10791-010-9161-5

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