Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
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
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
Emotion Classification Using Web Blog Corpora
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
SemEval-2010 task 18: Disambiguating sentiment ambiguous adjectives
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
MemeTube: a sentiment-based audiovisual system for analyzing and displaying microblog messages
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Customer review summarization approach using Twitter and SentiWordNet
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
Keyword-Based Sentiment Mining using Twitter
International Journal of Ambient Computing and Intelligence
The impact of social and conventional media on firm equity value: A sentiment analysis approach
Decision Support Systems
SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives
Language Resources and Evaluation
Twitter n-gram corpus with demographic metadata
Language Resources and Evaluation
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In this paper, we describe our system which participated in the SemEval 2010 task of disambiguating sentiment ambiguous adjectives for Chinese. Our system uses text messages from Twitter, a popular microblogging platform, for building a dataset of emotional texts. Using the built dataset, the system classifies the meaning of adjectives into positive or negative sentiment polarity according to the given context. Our approach is fully automatic. It does not require any additional hand-built language resources and it is language independent.