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.
['Koji Wajima', 'Tetsuji Satoh']
Urgent question detection based on the review points and sentiment words