JU_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 370–374, Dublin, Ireland, August 23-24, 2014. JU_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis Braja Gopal Patra, Soumik Mandal, Dipankar Das and Sivaji Bandyopadhyay Department of Computer Science & Engineering, Jadavpur University, Kolkata, India brajagopal.cse@gmail.com, mandal.soumik@gmail.com, dipankar.dipnil2005@gmail.com, sivaji_cse_ju@yahoo.com Abstract The fast upswing of online reviews and their sentiments on the Web became very useful information to the people. Thus, the opin- ion/sentiment mining has been adopted as a subject of increasingly research interest in the recent years. Being a participant in the Shared Task Challenge, we have developed a Conditional Random Field based system to accomplish the Aspect Based Sentiment Analysis task. The aspect term in a sentence is defined as the target entity. The present system identifies aspect term, aspect catego- ries and their sentiments from the Laptop and Restaurants review datasets provided by the organizers. 1 Introduction In recent times, the research activities in the areas of Opinion Mining/Sentiment Analysis in natural language texts and other media are gain- ing ground under the umbrella of subjectivity analysis and affect computing1. The reason may be the huge amount of available text data in So- cial Web in the forms of news, reviews, blogs, chat and twitter etc. Majority of research efforts are being carried out for the identification of pos- itive or negative polarity from the textual con- tents like sentence, paragraph, or text span re- gardless of the entities (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, ser- vice). This work is licensed under a Creative Commons At- tribution 4.0 International Licence. Page numbers and pro- ceedings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/ 1http://www.saaip.org/ Aspect is a multinomial distribution over words that represent a more specific topic in re- views (Jo and Oh, 2011). For example, in case of Laptop reviews, “touchpad” is considered an aspect. Similarly, given a predefined entity, an aspect term describes a specific aspect of that entity (e.g., for the entity “restaurant”, “wine” can be an aspect term). Aspect term can be ap- peared as a single word (e.g., “menu”) or multi- ple words (“side dish”). It is observed that for a particular entity, one or more number of aspect terms can be grouped into a single category (e.g., aspect terms “drinks”, “main course” belongs to the same cat- egory, “food”). The main goal of the Aspect Based Sentiment Analysis (ABSA) (Pontiki et al., 2014) task is to identify the aspect terms and their categories from the given target entities as well as to identi- fy the sentiments expressed towards each of the aspect terms. The datasets provided by the shared task organizers consist of customer re- views with human-annotations. We have participated in all of the four tasks. A combination of Conditional Random Field (CRF) based machine learning algorithm and rule based techniques has been adopted for identifying the aspect term, aspect category and their senti- ments. We have used several …

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