ILP-based Opinion Sentence Extraction from User Reviews for Question DB Construction

Author: Masakatsu Hamashita, Takashi Inui, Koji Murakami, Keiji Shinzato


Typical systems for analyzing users’ opinions from online product reviews have been researched and developed successfully. However, it is still hard to obtain sufficient user opinions when many reviews consist of short messages. This problem can be solved with an active opinion acquisition (AOA) framework that has an interactive interface and can elicit additional opinions from users. In this paper, we propose a method for automatically constructing a question database (QDB) essentialfor an AOA. In particular, to eliminate noisy sentences, we discuss a model for extracting opinion sentences that is formulated as a maximum coverage problem. Our proposed model has two advantages: (1) excluding redundant questions from a QDB while keeping variations of questions and (2) preferring simple sentence structures suitable for the question generation process. Our experimental results show that the proposed method achieved a precision of 0.88. We also give details on the optimal combination of model parameters.

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