Context Extraction for Aspect-Based Sentiment Analytics: Combining Syntactic, Lexical and Sentiment Knowledge

Abstract

Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services and also formulate critical action steps to improve their performance. In this work we focus on aspect-level sentiment classification studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We introduce a novel method that combines lexical, syntactical and sentiment knowledge effectively to extract opinion context for aspects. Thereafter we validate the quality of the opinion contexts extracted with human judgments using the BLEU score. Further we evaluate the usefulness of the opinion contexts for aspect-sentiment analysis. Our experiments on benchmark data sets from SemEval and a real-world dataset from the insurance domain suggests that extracting the right opinion context combining syntactical with sentiment co-occurrence knowledge leads to the best aspect-sentiment classification performance. From a commercial point of view, accurate aspect extraction, provides an elegant means to identify “pain-points” in a business. Integrating our work into a commercial CX platform (https://www.sentisum.com/) is enabling the company’s clients to better understand their customer opinions.