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Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary

Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary

Myint Zaw, Pichaya Tandayya
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 22
ISSN: 1935-5688|EISSN: 1935-5696|EISBN13: 9781683180302|DOI: 10.4018/IJISSS.2022010103
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MLA

Zaw, Myint, and Pichaya Tandayya. "Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary." IJISSS vol.14, no.1 2022: pp.1-22. http://doi.org/10.4018/IJISSS.2022010103

APA

Zaw, M. & Tandayya, P. (2022). Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary. International Journal of Information Systems in the Service Sector (IJISSS), 14(1), 1-22. http://doi.org/10.4018/IJISSS.2022010103

Chicago

Zaw, Myint, and Pichaya Tandayya. "Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary," International Journal of Information Systems in the Service Sector (IJISSS) 14, no.1: 1-22. http://doi.org/10.4018/IJISSS.2022010103

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Abstract

The customer feedbacks provide alternative and important sources to discover knowledge supporting the marketers and customers to make better decisions. However, the manual process to extract useful information depends on domain experts. This paper focuses on improving the performance of the automatic sentiment information extraction from customer feedbacks. The article proposes a new extraction method that consider multiple dimensions of feedback information, aspect, word, contrast, sentence or phrase, and document levels. The aspect-based sentiment extraction uses a named entity recognition technique to extract the desired aspects of a target product. The aspect-based sentiment combines with sentiment information from multiple levels of feedback contexts resulting in the fused sentiment information improves the extraction performance. We validate the effectiveness by measuring the accuracy of the sentiment and aspect recognition methods comparing with SentiStrength and Word-Count. This information gives some insights on customer satisfaction and can be applied in an alarming tool.

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