A maximum entropy approach to natural language processing
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
Semi-supervised polarity lexicon induction
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Learning with compositional semantics as structural inference for subsentential sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Identifying expressions of opinion in context
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The viability of web-derived polarity lexicons
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Recognizing stances in ideological on-line debates
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
Discovering fine-grained sentiment with latent variable structured prediction models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Don't turn social media into another 'Literary Digest' poll
Communications of the ACM
Extracting social power relationships from natural language
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Twitter polarity classification with label propagation over lexical links and the follower graph
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Comparison of feature-level learning methods for mining online consumer reviews
Expert Systems with Applications: An International Journal
Building a sentiment lexicon for social judgement mining
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
A weakly supervised model for sentence-level semantic orientation analysis with multiple experts
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Generating contextualized sentiment lexica based on latent topics and user ratings
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Automatic construction of domain and aspect specific sentiment lexicons for customer review mining
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Polarity lexicons have been a valuable resource for sentiment analysis and opinion mining. There are a number of such lexical resources available, but it is often suboptimal to use them as is, because general purpose lexical resources do not reflect domain-specific lexical usage. In this paper, we propose a novel method based on integer linear programming that can adapt an existing lexicon into a new one to reflect the characteristics of the data more directly. In particular, our method collectively considers the relations among words and opinion expressions to derive the most likely polarity of each lexical item (positive, neutral, negative, or negator) for the given domain. Experimental results show that our lexicon adaptation technique improves the performance of fine-grained polarity classification.