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Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge.

Bandhakavi, Anil; Wiratunga, Nirmalie; Massie, Stewart; Luhar, Rushi

Authors

Anil Bandhakavi

Rushi Luhar



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.

Citation

BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and LUHAR, R. 2018. Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 357-371. Available from: https://doi.org/10.1007/978-3-030-04191-5_30

Conference Name 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018)
Conference Location Cambridge, UK
Start Date Dec 11, 2018
End Date Dec 13, 2018
Acceptance Date Sep 3, 2018
Online Publication Date Nov 16, 2018
Publication Date Dec 31, 2018
Deposit Date Jan 8, 2018
Publicly Available Date Feb 8, 2019
Publisher Springer
Pages 357-371
Series Title Lecture notes in computer science
Series Number 11311
Series ISSN 0302-9743
Book Title Artificial intelligence XXXV
ISBN 9783030041908
DOI https://doi.org/10.1007/978-3-030-04191-5_30
Keywords Aspect extraction; Sentiment analysis; Natural language processing; Machine learning
Public URL http://hdl.handle.net/10059/3282

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