WordNet: a lexical database for English
Communications of the ACM
Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
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
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
A corpus study of evaluative and speculative language
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
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
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Identifying the semantic orientation of foreign words
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Mining slang and urban opinion words and phrases from cQA services: an optimization approach
Proceedings of the fifth ACM international conference on Web search and data mining
Structural opinion mining for graph-based sentiment representation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Computationally effective algorithm for information extraction and online review mining
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media
ACM Transactions on Intelligent Systems and Technology (TIST)
AttitudeMiner: mining attitude from online discussions
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session
Subgroup detection in ideological discussions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Extracting signed social networks from text
TextGraphs-7 '12 Workshop Proceedings of TextGraphs-7 on Graph-based Methods for Natural Language Processing
Mining sentiment terminology through time
Proceedings of the 21st ACM international conference on Information and knowledge management
EmotiWord: affective lexicon creation with application to interaction and multimedia data
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Assembling the optimal sentiment classifiers
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Constructing chinese sentiment lexicon using bilingual information
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Word polarity detection using a multilingual approach
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Random walks down the mention graphs for event coreference resolution
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
A method of feature selection and sentiment similarity for Chinese micro-blogs
Journal of Information Science
Ranked WordNet graph for Sentiment Polarity Classification in Twitter
Computer Speech and Language
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Automatically identifying the polarity of words is a very important task in Natural Language Processing. It has applications in text classification, text filtering, analysis of product review, analysis of responses to surveys, and mining online discussions. We propose a method for identifying the polarity of words. We apply a Markov random walk model to a large word related-ness graph, producing a polarity estimate for any given word. A key advantage of the model is its ability to accurately and quickly assign a polarity sign and magnitude to any word. The method could be used both in a semi-supervised setting where a training set of labeled words is used, and in an unsupervised setting where a handful of seeds is used to define the two polarity classes. The method is experimentally tested using a manually labeled set of positive and negative words. It outperforms the state of the art methods in the semi-supervised setting. The results in the unsupervised setting is comparable to the best reported values. However, the proposed method is faster and does not need a large corpus.