Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
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
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
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
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
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
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and 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
Comparative experiments on sentiment classification for online product reviews
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Subjectivity word sense disambiguation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Identifying text polarity using random walks
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Employing personal/impersonal views in supervised and semi-supervised sentiment classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
Multi-domain sentiment classification with classifier combination
Journal of Computer Science and Technology - Special issue on natural language processing
Semi-supervised learning for imbalanced sentiment classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Currently, sentiment analysis has become a hot research topic in the natural language processing (NLP) field as it is highly valuable for many practical usages and theoretical studies. As a basic task in sentiment analysis, construction of sentiment lexicon aims to classify one word into positive, neutral or negative according to its sentiment orientation. However, when constructing a sentiment lexicon in Chinese, there are two major problems: 1) Chinese words are very ambiguous, which makes it hard to compute the sentiment orientation of a word; 2) Given the related research on sentiment analysis, available resource for constructing Chinese sentiment lexicons remains weak. Note that there are several corpus and lexicons in English sentiment analysis. In this study, we first use machine translation system with bilingual resources, i.e., English and Chinese information, then we get the sentiment orientation of Chinese words by computing the point-wise mutual information (PMI) values with English seed words. Experiment results from three domains demonstrate that the lexicon generated with our approach reaches an excellent precision and could cover domain information effectively.