クジラ捕食戦略の強化によるツイートの感情分類のための最適化された階層的なCLSTMモデル
T Nithya1, M Siva Ramkumar2, Rajendran Thavasimuthu3
1Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India. nithya.t@ritchennai.edu.in.
Scientific reports
|August 29, 2025
まとめ
この研究は,ソーシャル・メディアにおける感情の分析を強化するための最適階層のコンボリューション・ニューラル・ロング・ショート・ターム・メモリー (OTCNLSTM) モデルを導入しています. OTCNLSTMモデルは,ツイートの感情を分類する精度を大幅に向上させ,既存の方法を上回ります.
科学分野:
- 自然言語処理
- 機械学習
- コンピュータ言語学
背景:
- ソーシャルメディアは 膨大なユーザー生成コンテンツを生み出し 意見採掘の複雑さを高めています
- 既存のセンチメント分析 (SA) システムは,データ制限と複雑なモデル構成により,予測の精度に問題があり,ディープラーニング (DL) アプリケーションを妨げています.
- 正確なSAは,製品,進歩,社会問題に関する世論を理解するために不可欠です.
研究 の 目的:
- ツイートにおける感情を分類するための改善されたセンチメント分析モデルを開発する.
- ディープラーニングモデルの低精度および予測率などの現在のSAシステムの限界に対処する.
- テキストデータからローカルな感情を階層的に抽出することを強化します.
主な方法:
- 感情認識のための分類学習を備えた最適階層型コンボリューションニューラル短期記憶 (OTCNLSTM) モデルを提案した.
- TCNLSTMモデル内の4つのトレーニングブロックを使用して,階層的なローカル特徴の抽出を行いました.
- ハイパーパラメータ最適化と安定したニューラルネットワークモデルの構築のためのブーストキラー鯨捕食 (BKWOP) 戦略を実装した.
- モデルパフォーマンスを評価するためにKaggle Twitterデータセットを使用して比較実験を行いました.
主要な成果:
- OTCNLSTMモデルは,他のセンチメント分析モデルと比較して,ツイートの感情を分類する上で優れたパフォーマンスを示しました.
- 提案されたモデルは 地元の感情を階層的に効果的に抽出します
- BKWOP戦略は,安定したニューラルネットワークを構築するための最適なハイパーパラメータを成功裏に特定しました.
結論:
- OTCNLSTMモデルは,ソーシャルメディアデータのセンチメント分析の精度を大幅に向上させています.
- 階層的な特徴抽出アプローチは テキストの微妙な感情を認識するモデルの能力を高める
- この研究により,リアルタイムで商用センチメント分析を行うための より堅牢で正確なソリューションが提供されます.
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