Tracking point of view in narrative
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
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SemEval-2010 task 18: Disambiguating sentiment ambiguous adjectives
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Predicting the uncertainty of sentiment adjectives in indirect answers
Proceedings of the 20th ACM international conference on Information and knowledge management
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The task of extracting the opinion expressed in text is challenging due to different reasons. One of them is that the same word (in particular, adjectives) can have different polarities depending on the context. This paper presents the experiments carried out by the OpAL team for the participation in the SemEval 2010 Task 18 -- Disambiguation of Sentiment Ambiguous Adjectives. Our approach is based on three different strategies: a) the evaluation of the polarity of the whole context using an opinion mining system; b) the assessment of the polarity of the local context, given by the combinations between the closest nouns and the adjective to be classified; c) rules aiming at refining the local semantics through the spotting of modifiers. The final decision for classification is taken according to the output of the majority of these three approaches. The method used yielded good results, the OpAL system run ranking fifth among 16 in micro accuracy and sixth in macro accuracy.