Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 753–758, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis Hussam Hamdan Aix-Marseille University hussam.hamdan@lsis.org Patrice Bellot Aix-Marseille University patrice.bellot@lsis.org Frederic Bechet Aix-Marseille University frederic.bechet@lif.univ-mrs.fr Abstract This paper describes our contribution in Opin- ion Target Extraction OTE and Sentiment Po- larity sub tasks of SemEval 2015 ABSA task. A CRF model with IOB notation has been adopted for OTE with several groups of fea- tures including syntactic, lexical, semantic, sentiment lexicon features. Our submission for OTE is ranked fifth over twenty submis- sions. A Logistic Regression model with a weighting schema of positive and negative la- bels have been used for sentiment polarity; several groups of features (lexical, syntac- tic, semantic, lexicon and Z score) are ex- tracted. Our submission for Sentiment Polar- ity is ranked third over ten submissions on the restaurant data set, third over thirteen on the laptops data set, but the first over eleven on the hotel data set that is out-of-domain set. 1 Introduction Sentiment Analysis (SA) has become more and more interesting since the year 2000, many tech- niques in Natural Language Processing have been used to understand the expressed sentiment on an entity. Many levels of granularity have been also distin- guished: Document Level SA considers the whole document is about an entity and classifies whether the expressed sentiment is positive, negative or neu- tral; Sentence Level SA determines the sentiment of each sentence, some papers have focused on Clause Level SA, but they are still not enough; Entity or Aspect-Based SA performs finer-grained analysis in which all entities and their aspects should be ex- tracted and the sentiment towards them should also be determined. Aspect-Based SA task consists of several sub- problems, the document is about many entities which could be for example a restaurant, a laptop, a printer. Users may refer to an entity by different writings, but normally there are not a lot of vari- ations to indicate the same entity, each entity has many aspects which could be its parts or attributes. Some aspects could be another entity such as screen of laptop, but most work did not take this case into account. Therefore, we could define the opinion by the quintuple (Liu, 2012) (ei, aij, sijkl, hk, tl) where ei is the entity i, aij are the aspects of the entity i, sijkl is the expressed sentiment on the aspect at the time tl , hk the holder which created the document or the text. This definition does not take into account that the entity has aspects that could have also other aspects which leads to an aspect hierarchy, in order to avoid this information loss, few work has handled this issue, they proposed to represent the aspect as a tree of aspect terms. In this paper, we focus on Opinion Target Extrac- tion (OTE) and Sentiment Polarity towards a target or …

Content truncated to 500 words