Abstract
Aspect-Based Sentiment Analysis (ABSA) is an advanced NLP application that aims to identify aspect terms present in the given review and predict the sentiment associated with those aspect terms. ABSA is better than sentence-based sentiment classification because it considers the aspect terms present in the reviews to determine the sentiment rather than considering the individual sentence. Entrepreneurs could make use of ABSA to understand the customers' opinions about different aspects of their products or services. The task of Aspect-based sentiment analysis can be divided into two subtasks: Aspect Term Extraction (ATE) and Aspect Term Sentiment Classification (ATSC). In this paper, an SVM model is proposed for the task of ATE, and an Attention based LSTM model is proposed for the task of ATSC. The proposed models will be trained and tested on SemEval-2014 dataset.
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