Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 140–144, Dublin, Ireland, August 23-24, 2014. Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014 Pavel Blinov, Eugeny Kotelnikov Vyatka State Humanities University {blinoff.pavel, kotelnikov.ev}@gmail.com Abstract The article describes our system submitted to the SemEval-2014 task on Aspect-Based Sentiment Analy- sis. The methods based on distribut- ed representations of words for the aspect term extraction and aspect term polarity detection tasks are pre- sented. The methods for the aspect category detection and category po- larity detection tasks are presented as well. Well-known skip-gram model for constructing the distribut- ed representations is briefly de- scribed. The results of our methods are shown in comparison with the baseline and the best result. 1 Introduction The sentiment analysis became an important Natural Language Processing (NLP) task in the recent few years. As many NLP tasks it’s a chal- lenging one. The sentiment analysis can be very helpful for some practical applications. For ex- ample, it allows to study the users’ opinions about a product automatically. Many research has been devoted to the general sentiment analysis (Pang et al., 2002), (Amine et al., 2013), (Blinov et al., 2013) or analysis of individual sentences (Yu and Hatzi- vassiloglou, 2003), (Kim and Hovy, 2004), (Wiebe and Riloff, 2005). Soon it became clear that the sentiment analysis on the level of a whole text or even sentences is too coarse. Gen- eral sentiment analysis by its design is not capa- ble to perform the detailed analysis of an ex- pressed opinion. For example, it cannot correctly detect the opinion in the sentence “Great food but the service was dreadful!”. The sentence car- ries opposite opinions on two facets of a restau- rant. Therefore the more detailed version of the sentiment analysis is needed. Such a version is called the aspect-based sentiment analysis and it works on the level of the significant aspects of the target entity (Liu, 2012). The aspect-based sentiment analysis includes two main subtasks: the aspect term extraction and its polarity detection (Liu, 2012). In this arti- cle we describe the methods which address both subtasks. The methods are based on the distribut- ed representations of words. Such word represen- tations (or word embeddings) are useful in many NLP task, e.g. (Turian et al., 2009), (Al- Rfou’ et al., 2013), (Turney, 2013). The remainder of the article is as follows: sec- tion two gives the overview of the data; the third section shortly describes the distributed represen- tations of words. The methods of the aspect term extraction and polarity detection are presented in the fourth and the fifth sections respectively. The conclusions are given in the sixth section. 2 The Data The organisers provided the train data for restau- rant and laptop domains. But as it will be clear further our methods are heavily dependent on unlabelled text data. So we additionally collected the user reviews about restaurants from tripad- viser.com and about laptops from amazon.com. General statistics of the …

Content truncated to 500 words