A vector space approach for aspect-based sentiment analysis

Abstract

Vector representations for language have been shown to be useful in a number of Natural Language Processing (NLP) tasks. In this thesis, we aim to investigate the effectiveness of word vector representations for the research problem of Aspect-Based Sentiment Analysis (ABSA), which attempts to capture both semantic and sentiment information encoded in user generated content such as product reviews. In particular, we target three ABSA sub-tasks: aspect term extraction, aspect category detection, and aspect sentiment prediction. We investigate the effectiveness of vector representations over different text data, and evaluate the quality of domain-dependent vectors. We utilize vector representations to compute various vector-based features and conduct extensive experiments to demonstrate their eectiveness. Using simple vector-based features, we achieve F1 scores of 79.9% for aspect term extraction, 86.7% for category detection, and 72.3% for aspect sentiment prediction.