BERTopic_Teen:青少年の健康における短いテキストトピックモデリングのためのマルチモジュール最適化アプローチ
Yiqiang Feng1, Ziao Chen2, Yuxin Zhang3
1School of Marxism, Sichuan Agricultural University, Chengdu, China.
Frontiers in public health
|August 28, 2025
まとめ
新しいフレームワーク"BERTopic_Teen"は ソーシャルメディア上の青少年健康に関するトピックモデリングを 強化しています ティーンエイジャーの健康に関する議論をよりよく理解することで 公共衛生の監視のためのデータ分析を改善します
科学分野:
- デジタル公衆衛生
- コンピュータ社会科学
- 青少年 の 健康 監視
背景:
- ソーシャル・メディアは公衆衛生に関するリアルタイムデータを 提供しています
- ソーシャルメディアの短いテキストは 伝統的なトピックモデリングに挑戦します
- オンラインでの若者の健康に関する議論には 専門的な分析ツールが必要です
研究 の 目的:
- 青少年の健康に関するツイートに最適化されたトピックモデリングフレームワークを開発する.
- ソーシャル・メディアのテキストの 短縮形式における意味論の希少性とノイズに対処するためです
- オンラインの青少年健康データからトピック抽出の正確性と安定性を向上させる.
主な方法:
- 提案されたBERTopic_Teenは,人気偏差調節器 (PDR),ダイナミック文書埋め込み最適化器 (DDEO),および確率再割り当てマトリックス (PRM) を含む.
- 64,441件の青少年による健康に関するツイートを 集めたデータです
- BERTopic_TeenとLDA,NMF,Top2Vec,そしてオリジナルのBERTopicを比較した.
主要な成果:
- BERTopic_Teenは,既存の方法と比較して,すべての評価指標で優れたパフォーマンスを示しました.
- テーマコヒーレンスが16.1%改善した (NPMI = 0.2184).
- トピックの多様性 (TD = 0.9935) が高く,困惑度が低い (1.7214).
結論:
- "BERTopic_Teen"は ソーシャルメディアで 青少年の健康問題を分析するための 強力なソリューションです
- フレームワークは意味論の明確性,トピックの特徴性,モデリングの安定性を高めます.
- 改善されたソーシャルメディアのデータ分析を通じて公衆衛生監視の進歩を 支援しています
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