Document-Word Co-regularization for Semi-supervised Sentiment Analysis

The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentiment prediction as a standard text categorization problem, but gathering labeled data turns out to be a bottleneck. Fortun...

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Bibliographic Details
Published in2008 Eighth IEEE International Conference on Data Mining pp. 1025 - 1030
Main Authors Sindhwani, V., Melville, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
Subjects
Blogs
Data mining
Discussion forums
Frequency
Graph Transduction
Linear models
Machine learning
Motion pictures
Prediction algorithms
Semi-supervised Learning
Sentiment Analysis
Text analysis
Text categorization
Vectors
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Summary:The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentiment prediction as a standard text categorization problem, but gathering labeled data turns out to be a bottleneck. Fortunately, background knowledge is often available in the form of prior information about the sentiment polarity of words in a lexicon. Moreover, in many applications abundant unlabeled data is also available. In this paper, we propose a novel semi-supervised sentiment prediction algorithm that utilizes lexical prior knowledge in conjunction with unlabeled examples. Our method is based on joint sentiment analysis of documents and words based on a bipartite graph representation of the data. We present an empirical study on a diverse collection of sentiment prediction problems which confirms that our semi-supervised lexical models significantly outperform purely supervised and competing semi-supervised techniques.
ISBN:076953502X
9780769535029
ISSN:1550-4786
2374-8486
DOI:10.1109/ICDM.2008.113