Affective computing
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for 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
Emotion Sensitive News Agent: An Approach Towards User Centric Emotion Sensing from the News
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Rules of Emotions: A Linguistic Interpretation of an Emotion Model for Affect Sensing from Texts
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
SenseRelate targetword: a generalized framework for word sense disambiguation
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
An Emotional and Context-Aware Model for Adapting RSS News to Users and Groups
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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The growth of content on the web has been followed by increasing interest in opinion mining. This field of research relies on accurate recognition of emotion from textual data. There's been much research in sentiment analysis lately, but it always focuses on the same elements. Sentiment analysis traditionally depends on linguistic corpora, or common sense knowledge bases, to provide extra dimensions of information to the text being analyzed. Previous research hasn't yet explored a fully automatic method to evaluate how events associated to certain entities may impact each individual's sentiment perception. This project presents a method to assign valence ratings to entities, using information from their Wikipedia page, and considering user preferences gathered from the user's Facebook profile. Furthermore, a new affective lexicon is compiled entirely from existing corpora, without any intervention from the coders.