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Title: Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12
Authors: Saias, José
Keywords: Sentiment Analysis
Machine Learning
Issue Date: 4-Jun-2015
Publisher: Association for Computational Linguistics
Citation: José Saias (2015). Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, USA. June 2015. p. 767-771, ACL
Abstract: This paper describes our participation in SemEval-2015 Task 12, and the opinion mining system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity. For slot 1, entity and attribute category detection, our system applies a supervised machine learning classifier, for each label, followed by a selection based on the probability of the entity/attribute pair, on that domain. The target expression detection, for slot 2, is achieved by using a catalog of known targets for each entity type, complemented with named entity recognition. In the opinion sentiment slot, we used a 3 class polarity classifier, having BoW, lemmas, bigrams after verbs, presence of polarized terms, and punctuation based features. Working in unconstrained mode, our results for slot 1 were assessed with precision between 57% and 63%, and recall varying between 42% and 47%. In sentiment polarity, sentiue’s result accuracy was approximately 79%, reaching the best score in 2 of the 3 domains.
Type: article
Appears in Collections:INF - Artigos em Livros de Actas/Proceedings

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