Analyzing and Summarizing Opinions in Conversations

2016 Fiscal Year Final Research Report

• Project/Area Number: 26280079
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Partial Multi-year Fund
• Section: 一般
• Research Field: Intelligent informatics
• Research Institution: Tokyo Institute of Technology
• Principal Investigator: Okumura Manabu 東京工業大学, 科学技術創成研究院, 教授 (60214079)
• Co-Investigator(Kenkyū-buntansha): 白井 清昭 北陸先端科学技術大学院大学, 情報科学研究科, 准教授 (30302970) ; 平尾 努 日本電信電話株式会社NTTコミュニケーション科学基礎研究所, その他部局等, 研究員 (40396148)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: ソーシャルメディア / 対話 / 意見分析 / 意見要約 / 議事録作成

Outline of Final Research Achievements

For the technologies for extracting and analyzing opinions, we developed a method for judging the importance of opinions, and presented a real-time tweet selection system for TV news programs. The system developed in this study collects tweets purporting to a TV news program, and chooses an appropriate tweet every 10 seconds. For the technologies for summarizing opinions, we formulated a summarization task as a combinatorial optimization problem, in which the nested tree was trimmed without losing important content in the source document. We used both dependency between words and dependency between sentences by constructing a nested tree. Next, we proposed methods for controlling the output sequence length for neural encoder-decoder models. Furthermore, we worked on Japanese sentence compression by a similar approach as in English.

Free Research Field

自然言語処理