|
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/14878
|
Title: | Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12 |
Authors: | Saias, José |
Keywords: | Sentiment Analysis NLP 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. |
URI: | http://alt.qcri.org/semeval2015/cdrom/pdf/SemEval130.pdf http://www.aclweb.org/anthology/S15-2130 http://hdl.handle.net/10174/14878 |
Type: | article |
Appears in Collections: | INF - Artigos em Livros de Actas/Proceedings
|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
|