BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
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
The Wisdom of Crowds
Computer
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
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
Games with a Purpose for the Semantic Web
IEEE Intelligent Systems
Programming collective intelligence
Programming collective intelligence
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Games with a purpose for social networking platforms
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Contextual phrase-level polarity analysis using lexical affect scoring and syntactic N-grams
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Integrating knowledge for subjectivity sense labeling
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Cross-Domain Contextualization of Sentiment Lexicons
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Sentiment analysis using a novel human computation game
Proceedings of the 3rd Workshop on the People's Web Meets NLP: Collaboratively Constructed Semantic Resources and their Applications to NLP
Heuristics for social games with a purpose
ICEC'12 Proceedings of the 11th international conference on Entertainment Computing
Serious questions in playful questionnaires
ICEC'12 Proceedings of the 11th international conference on Entertainment Computing
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Sentiment detection analyzes the positive or negative polarity of text. The field has received considerable attention in recent years, since it plays an important role in providing means to assess user opinions regarding an organization's products, services, or actions. Approaches towards sentiment detection include machine learning techniques as well as computationally less expensive methods. Both approaches rely on the use of language-specific sentiment lexicons, which are lists of sentiment terms with their corresponding sentiment value. The effort involved in creating, customizing, and extending sentiment lexicons is considerable, particularly if less common languages and domains are targeted without access to appropriate language resources. This paper proposes a semi-automatic approach for the creation of sentiment lexicons which assigns sentiment values to sentiment terms via crowd-sourcing. Furthermore, it introduces a bootstrapping process operating on unlabeled domain documents to extend the created lexicons, and to customize them according to the particular use case. This process considers sentiment terms as well as sentiment indicators occurring in the discourse surrounding a articular topic. Such indicators are associated with a positive or negative context in a particular domain, but might have a neutral connotation in other domains. A formal evaluation shows that bootstrapping considerably improves the method's recall. Automatically created lexicons yield a performance comparable to professionally created language resources such as the General Inquirer.