Texts generated by Web users are vehicles of facts, (i.e. objective expressions about entities, events and their attributes in he real world), as well as opinions, (i.e. subjective expressions of sentiments, feelings, attitudes, emotions or appraisals toward entities, events or attributes). Sentiment analysis (also called Opinion Mining) refers to the application of natural language processing and text analysis technologies to the identification and extraction of subjective information in source materials.
Related Projects and Resources
SemEval 2010, 2013
- (2014): A context based model for Sentiment Analysis in Twitter. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics, pp. 2345–2354, Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 2014.
- (2013): Robust Language Learning via Efficient Budgeted Online Algorithms. In: 3rd IEEE ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (SENTIRE), To Appear, Dallas, USA, 2013.
- (2013): UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pp. 369–374, Association for Computational Linguistics, Atlanta, Georgia, USA, 2013.
- (2013): Enabling Advanced Business Intelligence in Divino.. In: DART@AI*IA, pp. 61-72, 2013.