A maximum entropy approach to natural language processing
Computational Linguistics
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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SemEval-2010 task 18: Disambiguating sentiment ambiguous adjectives
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Revised mutual information approach for german text sentiment classification
Proceedings of the 22nd international conference on World Wide Web companion
SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives
Language Resources and Evaluation
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This paper describes our system participating in task 18 of SemEval-2010, i.e. disambiguating Sentiment-Ambiguous Adjectives (SAAs). To disambiguating SAAs, we compare the machine learning-based and lexicon-based methods in our submissions: 1) Maximum entropy is used to train classifiers based on the annotated Chinese data from the NTCIR opinion analysis tasks, and the clause-level and sentence-level classifiers are compared; 2) For the lexicon-based method, we first classify the adjectives into two classes: intensifiers (i.e. adjectives intensifying the intensity of context) and suppressors (i.e. adjectives decreasing the intensity of context), and then use the polarity of context to get the SAAs' contextual polarity based on a sentiment lexicon. The results show that the performance of maximum entropy is not quite high due to little training data; on the other hand, the lexicon-based method could improve the precision by considering the polarity of context.