SZTE-NLP: Aspect level opinion mining exploiting syntactic cues

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 610–614, Dublin, Ireland, August 23-24, 2014. SZTE-NLP: Aspect Level Opinion Mining Exploiting Syntactic Cues Viktor Hangya1, G´abor Berend1, Istv´an Varga2∗, Rich´ard Farkas1 1University of Szeged Department of Informatics {hangyav,berendg,rfarkas}@inf.u-szeged.hu 2NEC Corporation, Japan Knowledge Discovery Research Laboratories vistvan@az.jp.nec.com Abstract In this paper, we introduce our contribu- tions to the SemEval-2014 Task 4 – As- pect Based Sentiment Analysis (Pontiki et al., 2014) challenge. We participated in the aspect term polarity subtask where the goal was to classify opinions related to a given aspect into positive, negative, neutral or conflict classes. To solve this problem, we employed supervised ma- chine learning techniques exploiting a rich feature set. Our feature templates ex- ploited both phrase structure and depen- dency parses. 1 Introduction The booming volume of user-generated content and the consequent popularity growth of online re- view sites has led to vast amount of user reviews that are becoming increasingly difficult to grasp. There is desperate need for tools that can automat- ically process and organize information that might be useful for both users and commercial agents. Such early approaches have focused on deter- mining the overall polarity (e.g., positive, nega- tive, neutral, conflict) or sentiment rating (e.g., star rating) of various entities (e.g., restaurants, movies, etc.) cf. (Ganu et al., 2009). While the overall polarity rating regarding a certain entity is, without question, extremely valuable, it fails to distinguish between various crucial dimensions based on which an entity can be evaluated. Evalu- ations targeting distinct key aspects (i.e., function- ality, price, design, etc) provide important clues that may be targeted by users with different priori- ties concerning the entity in question, thus holding ∗The work was done while this author was working as a guest researcher at the University of Szeged This work is licensed under a Creative Commons At- tribution 4.0 International Licence. Page numbers and pro- ceedings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/ much greater value in one’s decision making pro- cess. In this paper, we introduce our contribution to the SemEval-2014 Task 4 – Aspect Based Sen- timent Analysis (Pontiki et al., 2014) challenge. We participated in the aspect term polarity sub- task where the goal was to classify opinions which are related to a given aspect into positive, nega- tive, neutral or conflict classes. We employed su- pervised machine learning techniques exploiting a rich feature set for target polarity detection, with a special emphasis on features that deal with the detection of aspect scopes. Our system achieved an accuracy of 0.752 and 0.669 for the restaurant and laptop domains, respectively. 2 Approach We employed a four-class supervised (positive, negative, neutral and conflict) classifier here. As a normalization step, we converted the given texts into their lowercased forms. Bag-of-words fea- tures comprised the basic feature set for our max- imum entropy classifier, which was shown to be helpful in polarity detection (Hangya and Farkas, 2013). In the case of aspect-oriented sentiment detec- tion, we …

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