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Title:
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ESTIMATING HELPFULNESS OF CUSTOMER REVIEW BY CONTENT COVERAGE AND WRITING STYLE |
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Author(s):
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Akihide Bessho, Takayuki Yumoto, Manabu Nii, Kunihiro Sato |
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ISBN:
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978-989-8533-24-1 |
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Editors:
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Pedro Isaías and Bebo White |
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Year:
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2014 |
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Edition:
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Single |
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Keywords:
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Customer review, helpfulness, coverage, writing style, support vector machine |
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Type:
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Full Paper |
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First Page:
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315 |
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Last Page:
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322 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Customer reviews are helpful information to decide what to buy in EC sites. However, some customer reviews are not helpful. Therefore, users must choose helpful ones. This is difficult especially for users who do not have enough knowledge about the products. In this paper, we propose methods to classify customer reviews into helpful and unhelpful. To classify them, we focus on content coverage and writing style of reviews. Coverage expresses how many important words are contained by the reviews. Writing style is expressed as formal or informal, and it is classified by a machine learning technique. We made test data from user votes in Amazon.co.jp, and evaluated our methods. The accuracy of our rule-based method was 0.69. |
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