AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews

Proceedings of SemEval-2016, pages 318–324, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews Shubham Pateria and Prafulla Kumar Choubey System Software Division Samsung R and D Institute India Bangalore Pvt Ltd Bangalore, Karanataka, India -560037 s.pateria, prafulla.ch@samsung.com Abstract This paper describes the polarity classification system designed for participation in SemEval- 2016 Task 5 - ABSA. The aim is to determine the sentiment polarity expressed towards cer- tain aspect within a consumer review. Our sys- tem is based on supervised learning using Sup- port Vector Machine (SVM). We use standard features for basic classification model. On top this, we include rules to check precedent po- larity sequence. This approach is experimen- tal. 1 Introduction In the consumer-focused markets today, understand- ing opinions expressed on the online platforms or review portals is of key essence for the businesses. Statistical or Machine learning methods and Natural Language Processing are now being widely applied to extract important information or patterns from the opinion data. A review statement may have a mix of sentiments towards different aspects. For e.g., consider the food and ambiance at xyz hotel were extraordinary, as expected. However, the waiters seemed rude. Here, the main entity of review is a ’hotel’. Henceforth, we will refer to such main en- tity as global item. However, there is no definite overall sentiment expressed towards the global item. Different sentiments are expressed towards food and ambiance aspects (extraordinary: positive) and to- wards the aspect of service (waiters, rude: negative). Thus, it is important to approach sentiment detection as an aspect-based problem. The SemEval-2016 Task 5 - Aspect Based Sen- timent Analysis (ABSA) focuses on this problem (Pontiki et al., 2016). This task is a continua- tion from SemEval-2015 ABSA task (Pontiki et al., 2015). The task was organized across different do- mains and languages. We participated in Restaurant domain in English language. The focus of our sys- tem is polarity detection and not aspect extraction. Thus, we use dataset in which aspects are already known. To develop our system, we have used standard features for basic model and also rules to check ef- fect of precedent-polarity sequence pattern on polar- ity to be predicted. We focus on experimenting with sequence pattern. The system is described in Sec- tion 3. Pre-processing is described in Sub-section 3.1. selected features are discussed in Sub-section 3.2 and sequence pattern discussed in Sub-section 3.3. In section 4, we discuss the analysis and evalu- ation results for our system. 2 Related Work Aspect-based sentiment analysis has been a sub- ject of some interesting works so far. (McAuley et al., 2012) employ topic modeling paradigm to ad- dress this problem. Deep Learning has also been explored in this area, such as by (Wang and Liu, 2015). They used Convolutional Neural Network for aspect-based analysis of SemEval-2015 ABSA data and reported performance comparable to top systems of the 2015 task. Previously, the system by (Kiritchenko et al., …

Content truncated to 500 words