IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis

Proceedings of SemEval-2016, pages 1129–1135, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis Ayush Kumar IIT Patna, India Dept. of Computer Science ayush.cs12@iitp.ac.in Sarah Kohail TU Darmstadt, Germany Computer Science Dept, Language Technology Group kohail@lt.informatik.tu-darmstadt.de Amit Kumar IIT Patna, India Dept. of Computer Science amit.mtmc14@iitp.ac.in Asif Ekbal IIT Patna, India Dept. of Computer Science asif@iitp.ac.in Chris Biemann TU Darmstadt, Germany Computer Science Dept, Language Technology Group biem@cs.tu-darmstadt.de Abstract This paper reports the IIT-TUDA participation in the SemEval 2016 shared Task 5 of Aspect Based Sentiment Analysis (ABSA) for sub- task 1. We describe our system incorporat- ing domain dependency graph features, dis- tributional thesaurus and unsupervised lexical induction using an unlabeled external corpus for aspect based sentiment analysis. Overall, we submitted 29 runs, covering 7 languages and 4 different domains. Our system is placed first in sentiment polarity classification for the English laptop domain, Spanish and Turkish restaurant reviews, and opinion target expres- sion for Dutch and French in restaurant do- main, and scores in medium ranks for aspect category identification and opinion target ex- traction. 1 Introduction The advent of web technologies has made an un- precedented opportunity for online users to share and explain their views and opinions. The co- relation between the views expressed by the users and the market strategies by the organizations strikes the importance of analyzing such reviews. But, valu- able as they are, user-generated review texts are un- structured and very noisy. Major research studies adopted Natural Language Processing (NLP) and text mining techniques to better understand and pro- cess various types of information in user-generated reviews. Such efforts have come to be known as opinion mining, sentiment analysis or review min- ing (Pang and Lee, 2008). Aspect level analysis performs a finer-grained sentiment analysis by addressing three sub- problems: extracting aspects from the review text, identifying the entity that is referred to by the aspect, and finally classifying the opinion polarity towards the aspect (Liu, 2012). For example, a review of the “entity” laptop is likely to discuss distinct “aspects” like size, processing unit, and memory, and a single product can trigger a positive “opinion” about one feature, and a negative “opinion” about another. In an attempt to support the efforts on Aspect Based Sentiment Analysis (ABSA), the SemEval 2016 shared Task 5 ABSA (Pontiki et al., 2016) offers the opportunity to experiment and evaluate on benchmark datasets (reviews) across various domains and languages through three subtasks. Subtask 1 covers the three sub-problems mentioned above, namely: aspect category identification (Slot 1), opinion target expression (OTE) (Slot 2) and sentiment polarity classification (Slot 3). We participated in Slot 1 and Slot 3 for English, Spanish, Dutch, French, Turkish, Russian and Arabic language for all available domains except telecoms. We also conducted experiments for Slot 2 for English, Spanish, Dutch and French. Overall, we submitted 29 runs, covering 7 languages and …

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