NDLI: Urgent question detection based on the review points and sentiment words

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Urgent question detection based on the review points and sentiment words

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Urgent question detection based on the review points and sentiment words

Content Provider	ACM Digital Library
Author	Wajima, Koji Satoh, Tetsuji
Abstract	To response to customers during business activities, it is required to quickly find out the important question articles that need to be responded to promptly. This paper offers a proposal of priority judgment method based on the sentiment words inside these question articles in cases of large amounts of questions to services or products. The proposed method uses LDA which is known as an "unsupervised learning technique" to extract question articles as the "question target", and then the question target part referred in questions, known as the "review points" are extracted by KeyGraph based on question targets. For those emotions of question articles' authors from extracted review points, evaluation expression dictionary is used to give "sentiment words" to question articles. For the composition of question targets and review points, question articles with more negative emotions are considered higher priority. The proposed method is applied to the question articles registered in the online support community to perform a priority judgment experiment of them. After comparing the ranking of priorities given by users with support center experience, it is proved that the proposed method could correctly judge articles with high priority and the sentiment words given to articles with medium priority are also effective. In addition, its accuracy has also been improved while comparing to previous studies.
Starting Page	1
Ending Page	5
Page Count	5
File Format	PDF
ISBN	9781450334914
DOI	10.1145/2837185.2837252
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-12-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Keygraph Lda Sentiment words Review points
Content Type	Text
Resource Type	Article

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