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Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts
https://tokushima-u.repo.nii.ac.jp/records/2010796
https://tokushima-u.repo.nii.ac.jp/records/2010796ac36bf04-3b3a-4e1b-9214-0ade3dc417f1
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
electronics_11_21_3431.pdf (2.59 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||||||||||||
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| 公開日 | 2023-03-17 | |||||||||||||||||||||||||||||
| アクセス権 | ||||||||||||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||||||||||||
| 関連識別子 | https://doi.org/10.3390/electronics11213431 | |||||||||||||||||||||||||||||
| 関連名称 | 10.3390/electronics11213431 | |||||||||||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||||||||
| タイトル | ||||||||||||||||||||||||||||||
| タイトル | Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts | |||||||||||||||||||||||||||||
| 著者 |
松本, 和幸
× 松本, 和幸
WEKO
311
× アミタニ, レイシ
× 吉田, 稔
WEKO
641
× 北, 研二
WEKO
94
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| 抄録 | ||||||||||||||||||||||||||||||
| 内容記述 | This paper propose a method to predict the stage of buzz-trend generation by analyzing the emotional information posted on social networking services for multimodal information, such as posted text and attached images, based on the content of the posts. The proposed method can analyze the diffusion scale from various angles, using only the information at the time of posting, when predicting in advance and the information of time error, when used for posterior analysis. Specifically, tweets and reply tweets were converted into vectors using the BERT general-purpose language model that was trained in advance, and the attached images were converted into feature vectors using a trained neural network model for image recognition. In addition, to analyze the emotional information of the posted content, we used a proprietary emotional analysis model to estimate emotions from tweets, reply tweets, and image features, which were then added to the input as emotional features. The results of the evaluation experiments showed that the proposed method, which added linguistic features (BERT vectors) and image features to tweets, achieved higher performance than the method using only a single feature. Although we could not observe the effectiveness of the emotional features, the more emotions a tweet and its reply match had, the more empathy action occurred and the larger the like and RT values tended to be, which could ultimately increase the likelihood of a tweet going viral. | |||||||||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||||||||
| 主題 | multi-modal buzz prediction | |||||||||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||||||||
| 主題 | information diffusion | |||||||||||||||||||||||||||||
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| 主題 | affective analysis | |||||||||||||||||||||||||||||
| 書誌情報 |
en : Electronics 巻 11, 号 21, p. 3431, 発行日 2022-10-23 |
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| 収録物識別子タイプ | ISSN | |||||||||||||||||||||||||||||
| 収録物識別子 | 20799292 | |||||||||||||||||||||||||||||
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| 出版者 | MDPI | |||||||||||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||||||||||
| 権利情報 | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | |||||||||||||||||||||||||||||
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| 識別子 | 392572 | |||||||||||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||||||||||
