SIEL: Aspect Based Sentiment Analysis in Reviews

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 759–766, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics SIEL: Aspect Based Sentiment Analysis in Reviews Satarupa Guha, Aditya Joshi, Vasudeva Varma Search and Information Extraction Lab International Institute of Information Technology, Hyderabad Gachibowli, Hyderabad, Telengana, India {satarupa.guha,aditya.joshi}@research.iiit.ac.in vv@iiit.ac.in Abstract Following the footsteps of SemEval-2014 Task 4 (Pontiki et al., 2014), SemEval-2015 too had a task dedicated to aspect-level senti- ment analysis (Pontiki et al., 2015), which saw participation from over 25 teams. In Aspect- based Sentiment Analysis, the aim is to iden- tify the aspects of entities and the sentiment expressed for each aspect. In this paper, we present a detailed description of our system, that stood 4th in Aspect Category subtask (slot 1), 7th in Opinion Target Expression subtask (slot 2) and 8th in Sentiment Polarity subtask (slot 3) on the Restaurant datasets. 1 Introduction When a review or a social media post talks about a product or service, the user might want to dis- cuss multiple aspects or sub-topics related to the product or service being discussed. For example, in a restaurant review, while the customer might have good things to say about the food quality of- fered at a restaurant, she might be disappointed with the service offered to her, and she might think the decor needs to be revamped. So a general senti- ment analyzer that determines the overall sentiment towards the product or service might not be able to capture the full essence of the review. Hence the need for Aspect-based Sentiment Analysis, for bet- ter and more fine-grained analysis of user feedback, which would enable service providers and product manufacturers to identify those business aspects that needs improvement. Specifically, SemEval-2015 Task 12 expects systems to automatically determine the aspect categories present in the data and the sen- timent expressed towards each of those categories, given a customer review. For the Aspect Category (Entity and Attribute) Detection subtask, one has to identify every entity E and attribute A pair E#A to- wards which an opinion is expressed in the given text. E and A should be chosen from predefined inventories of Entity types and Attribute labels per domain. Each E#A pair together defines an aspect category of the given text. The E#A inventories for the restaurants domain has been shown in Table 1. For the Opinion Target Expression (OTE) identi- fication subtask (Slot 2), we need to identify an ex- pression used in the given text that refers to the re- viewed entity E of a pair E#A. The OTE is defined by its starting and ending offsets in the given text. The OTE slot takes the value “NULL” when there is no explicit mention of the opinion entity or no men- tion at all. For Sentiment Polarity Detection task, each iden- tified E#A pair of the given text has to be assigned a polarity - positive, negative, or neutral. 2 Related Work The Aspect Category Detection task can be …

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