WordNet: a lexical database for English
Communications of the ACM
Affective computing
FLAME—Fuzzy Logic Adaptive Model of Emotions
Autonomous Agents and Multi-Agent Systems
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Web science: an interdisciplinary approach to understanding the web
Communications of the ACM - Web science
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Textual demand analysis: detection of users' wants and needs from opinions
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
Sentic computing: exploitation of common sense for the development of emotion-sensitive systems
COST'09 Proceedings of the Second international conference on Development of Multimodal Interfaces: active Listening and Synchrony
Affect analysis of text using fuzzy semantic typing
IEEE Transactions on Fuzzy Systems
Semantic sentiment analysis of twitter
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
New Avenues in Opinion Mining and Sentiment Analysis
IEEE Intelligent Systems
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Capturing the sentiments and the emotional states enclosed in textual information is a critical task which embraces a wide range of web-oriented activities such as detecting the sentiments associated to the product reviews, developing marketing programs that would be attractive for users, enhancing customer service with respect to its expectation until to identifying new opportunities and financial market prediction, besides managing reputations. Opinions and the emotions that are embedded in them, play a key role in decision-making processes, with different effects depending on the negative or positive valence of the mood. When the choice depends on some important features (i.e., time, money, reliability/efficacy, etc.) and on other opinions (which come from previous experience), could be crucial to make the best decision. Inferring opinions and emotions enclosed in the written language is a complex task which cannot rely on body languages (posture, gestures, vocal inflections), rather than discovering concepts with an affective valence. The role of opinions extracted by the social content is crucial to support consumers' decision process; in addition, thanks opinions and emotions, it is possible to evidence improvements on existing decision supports and show how the opinion-mining techniques can be incorporated into these systems. This paper presents a tentative contribution that addresses this issue: it introduces a framework for extracting the emotions and the sentiments expressed in the textual data. The sentiments are expressed by a positive or negative polarity, the emotions are based on the Minsky's conception of emotions, that consists of four affective dimensions, each one with six levels of activations [1]. Sentiments and emotions are modeled as fuzzy sets; particularly, the intensity of the emotions has been tuned by fuzzy modifiers, which act on the linguistic patterns recognized in the sentences. The approach has been tested on some sets of documents categories, revealing interesting performance on the global framework processing.