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QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyond
April 30, 2024, 4:50 a.m. | Tomoki Fukuma, Koki Noda, Toshihide Ubukata Kousuke Hoso, Yoshiharu Ichikawa, Kyosuke Kambe, Yu Masubuch, Fujio Toriumi
cs.CL updates on arXiv.org arxiv.org
Abstract: The proliferation of social media has led to information overload and increased interest in opinion mining. We propose "Question-Answering Network Analysis" (QANA), a novel opinion mining framework that utilizes Large Language Models (LLMs) to generate questions from users' comments, constructs a bipartite graph based on the comments' answerability to the questions, and applies centrality measures to examine the importance of opinions. We investigate the impact of question generation styles, LLM selections, and the choice of …
abstract analysis arxiv beyond cs.cl framework generate information key language language models large language large language models llm llms media mining network novel opinion overload question questions social social media type zero-shot
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