| 研究課題/領域番号 |
23K16954
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分61030:知能情報学関連
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| 研究機関 | 統計数理研究所 |
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研究代表者 |
Tran Duc・Vu 統計数理研究所, リスク解析戦略研究センター, 特任助教 (90910240)
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2025年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2024年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2023年度: 2,860千円 (直接経費: 2,200千円、間接経費: 660千円)
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| キーワード | large language models / effective prompting / sentiment analysis / social media data / public sentiments / social media / NLP / deep learning / statistical modeling |
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| 研究開始時の研究の概要 |
It is a research on predicting sentiments of random social media users and contributes to the research direction on assessing public sentiments in a manner similar to conducting questionnaire surveys, timely, progressively, and low-cost, and applicable in economics, public health, and politics.
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| 研究実績の概要 |
In the first year of the research, promising results have been achieved for sentiment analysis using advanced natural language processing techniques on social media data. Especially, with the emergence of even more powerful large language models available for use and for fine-tuning to the public. Experiments were conducted with several high-end large language models on both Twitter ("X") and Reddit data for analyzing users. Experimental results showed that large language models can achieve good performance of analyzing social media texts with optimally designed prompting techniques, a way to make effective inputs to large language models. The results have been published in international conferences/workshops.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
From the large social media data collected for the research, several subsets have been analyzed to obtain topic-based sentiment using large language models with optimally designed prompting techniques. A number of large language models are available for utilization internally at the research institute: Llama, Mixtral, StableLM, etc. ChatGPT-4 is also utilized for sentiment analysis.
After the acquisition of Twitter Inc. (now called "X") by Elon Musk, in 2023, Twitter stoped its api for scientific research. Due to the incident, large Twitter data is no longer available via its service API at low cost. Nevertheless, abundant Twitter data was collected beforehand and is to be used continuously.
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| 今後の研究の推進方策 |
In the second year of the research, techniques for modeling social relationships for users and topics are to be investigated. From the current experimental results, ensemble of multiple sentiment analysis tools is an effective way to have more accurate analysis outputs which are the inputs to the social relationship models.
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