Clause-level Analysis High-value Reviews based on Sentiment

Today, huge numbers of reviews are posted on the internet. Online shoppers often refer to reviews written about the products. A review has a star rating that represents what other people think about the product. However, the star rating is not always appropriate for evaluating the product. High-valu...

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Published inJournal of data intelligence Vol. 1; no. 4; pp. 468 - 488
Main Authors Nadamoto, Akiyo, Akiyama, Kazuhiro, Kumamoto, Tadahiko
Format Journal Article
LanguageEnglish
Japanese
Published 01.12.2020
Online AccessGet full text
ISSN2577-610X
2577-610X
DOI10.26421/JDI1.4-4

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Abstract Today, huge numbers of reviews are posted on the internet. Online shoppers often refer to reviews written about the products. A review has a star rating that represents what other people think about the product. However, the star rating is not always appropriate for evaluating the product. High-value reviews that affect the users' willingness to buy are independent of the number of stars in ratings. High-value reviews are those from which people find useful information those regarded as good reviews. As described in this paper, we investigated the relation between high-value reviews and the sentiment (positive/negative/neutral) of their clauses based on four hypotheses. We extract characteristics of high-value reviews based on our results. Furthermore, we propose a classification method that classifies clause level sentiment from reviews.
AbstractList Today, huge numbers of reviews are posted on the internet. Online shoppers often refer to reviews written about the products. A review has a star rating that represents what other people think about the product. However, the star rating is not always appropriate for evaluating the product. High-value reviews that affect the users' willingness to buy are independent of the number of stars in ratings. High-value reviews are those from which people find useful information those regarded as good reviews. As described in this paper, we investigated the relation between high-value reviews and the sentiment (positive/negative/neutral) of their clauses based on four hypotheses. We extract characteristics of high-value reviews based on our results. Furthermore, we propose a classification method that classifies clause level sentiment from reviews.
Author Kumamoto, Tadahiko
Akiyama, Kazuhiro
Nadamoto, Akiyo
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