Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Text classification by labeling words
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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Sentiment analysis of documents aims to characterise the positive or negative sentiment expressed in documents. It has been formulated as a supervised classification problem, which requires large numbers of labelled documents. Semi-supervised sentiment classification using limited documents or words labelled with sentiment-polarities are approaches to reducing labelling cost for effective learning. Expectation Maximisation (EM) has been widely used in semi-supervised sentiment classification. A prominent problem with existing EM-based approaches is that the objective function of EM may not conform to the intended classification task and thus can result in poor classification performance. In this paper we propose to augment EM with the lexical knowledge of opinion words to mitigate this problem. Extensive experiments on diverse domains show that our lexical EM algorithm achieves significantly higher accuracy than existing standard EM-based semi-supervised learning approaches for sentiment classification, and also significantly outperforms alternative approaches using the lexical knowledge.