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Discovering Attribute-Specific Features From Online Reviews: What Is the Gap Between Automated Tools and Human Cognition?

Discovering Attribute-Specific Features From Online Reviews: What Is the Gap Between Automated Tools and Human Cognition?

Xiaonan Jing, Penghao Wang, Julia M. Rayz
Copyright: © 2018 |Volume: 10 |Issue: 2 |Pages: 24
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781522544036|DOI: 10.4018/IJSSCI.2018040101
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MLA

Jing, Xiaonan, et al. "Discovering Attribute-Specific Features From Online Reviews: What Is the Gap Between Automated Tools and Human Cognition?." IJSSCI vol.10, no.2 2018: pp.1-24. http://doi.org/10.4018/IJSSCI.2018040101

APA

Jing, X., Wang, P., & Rayz, J. M. (2018). Discovering Attribute-Specific Features From Online Reviews: What Is the Gap Between Automated Tools and Human Cognition?. International Journal of Software Science and Computational Intelligence (IJSSCI), 10(2), 1-24. http://doi.org/10.4018/IJSSCI.2018040101

Chicago

Jing, Xiaonan, Penghao Wang, and Julia M. Rayz. "Discovering Attribute-Specific Features From Online Reviews: What Is the Gap Between Automated Tools and Human Cognition?," International Journal of Software Science and Computational Intelligence (IJSSCI) 10, no.2: 1-24. http://doi.org/10.4018/IJSSCI.2018040101

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Abstract

This article describes how online reviews play an important role in data driven decision making. Many efforts have been invested in determining the overall sentiment carried by the reviews. However, oftentimes, the overall ratings of the reviews do not represent opinions toward specific attributes of a product. An ideal opinion mining tool should aim at finding both the product attributes and their corresponding opinions. The authors propose an approach for extracting the attribute specific features from online reviews using a Word2Vec model combined with clustering. Two experiments are described in this paper: the first focuses on testing the performance of the Word2Vec model on extracting product aspect words, the second addresses how well the extracted features obtained are recognizable by human cognition. A new metric called the “split value” that is based on cluster similarity and diversity is introduced to examine the consistency of clustering algorithm. The authors' experiments suggest that meaningful clusters, which provide insights to the product attributes and sentiments, could be extracted from the reviews.

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