SAP-RI: A Constrained and Supervised Approach for Aspect-Based Sentiment Analysis

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 517–521, Dublin, Ireland, August 23-24, 2014. SAP-RI: A Constrained and Supervised Approach for Aspect-Based Sentiment Analysis Nishtha Malhotra1,2∗, Akriti Vij1,2,∗, Naveen Nandan1 and Daniel Dahlmeier1 1Research & Innovation, SAP Asia, Singapore 2Nanyang Technological University, Singapore {nishtha.malhotra,akriti.vij,naveen.nandan,d.dahlmeier}@sap.com Abstract We describe the submission of the SAP Research & Innovation team to the Se- mEval 2014 Task 4: Aspect-Based Senti- ment Analysis (ABSA). Our system fol- lows a constrained and supervised ap- proach for aspect term extraction, catego- rization and sentiment classification of on- line reviews and the details are included in this paper. 1 Introduction The increasing popularity of the internet as a source of information, and e-commerce as a way of life, has led to a major surge in the number of reviews that can be found online, for a wide range of products and services. Consequently, more and more consumers have taken to consulting these on- line reviews as part of their pre-purchase research before deciding on availing services from a local business or investing in a product from a particu- lar brand. This calls for innovative techniques for the sentiment analysis of online reviews so as to generate accurate and relevant recommendations. Sentiment analysis has been extensively studied and applied in different domains. Predicting the sentiment polarity (positive, negative, neutral) of user opinions by mining user reviews (Hu and Liu, 2004; Liu, 2012; Pang and Lee, 2008; Liu, 2010) has been of high commercial and research interest. In these studies, sentiment analysis is often con- ducted at one of the three levels: document level, sentence level or attribute level. Through the SemEval 2014 Task 4 on Aspect Based Sentiment Analysis (Pontiki et al., 2014), we explore sentiment analysis at the aspect level. ∗ The work was done during an internship at SAP. This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http: //creativecommons.org/licenses/by/4.0/ The task consists of four subtasks: in subtask 1 as- pect term extraction, participants need to identify the aspect terms present in a sentence and return a list containing all distinct aspect terms, in sub- task 2 aspect term polarity, participants were to determine the polarity of each aspect term in a sen- tence, in subtask 3 aspect category detection, par- ticipants had to identify the aspect categories dis- cussed in a given sentence, and in subtask 4 aspect category polarity, participants were to determine the polarity of each aspect category. The polarity classification subtasks consider sentiment analysis to be a three-way classification problem between positive, negative and neutral sentiment. On the other hand, the aspect category detection subtask is a multi-label classification problem where one sentence can be labelled with more than one as- pect category. In this paper, we describe the submission of the SAP-RI team to the SemEval 2014 Task 4. We make use of supervised techniques to extract the aspects of interest (Jakob and Gurevych, …

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