KRISTIANTO, ERICK (2024) A SENTIMENT POLARITY ANALYSIS OF INTERNATIONAL ENGLISH-SPEAKING FANS' REACTIONS TO 'ONE PIECE' LIVE-ACTION ADAPTATION. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
With the increasing popularity of live-action adaptations of beloved anime series, understanding fan sentiments is essential. This research employs sentiment analysis to explore the perceptions of international English-speaking fans regarding the "One Piece" live-action adaptation. Utilizing the Python Reddit API Wrapper (PRAW) and machine learning techniques, we analyze Reddit discussions, employing Support Vector Machines (SVM) and Random Forest algorithms for sentiment classification.Our findings reveal that the majority of fan comments express positive sentiments towards the "One Piece" live-action adaptation, with 8790 instances of positive sentiment compared to 5498 instances of negative sentiment. The SVM model using SGDClassifier emerged as the top performer, demonstrating the highest precision and Cohen's Kappa score among the evaluated models. This study contributes to Information Technology and media studies by offering insights into fan sentiments and the accuracy of sentiment analysis methods. It informs decision-making for producers, addresses real-world implications, and bridges gaps for stakeholders. Additionally, it enhances academic understanding of sentiment dynamics within fan communities, particularly concerning liveaction anime adaptations. Keywords: Sentiment Analysis, One Piece, Live-Action Adaptation, Machine Learning, SVM, Random Forest Dengan semakin populernya adaptasi live-action dari serial anime yang dicintai, memahami sentimen penggemar menjadi sangat penting. Penelitian ini menggunakan analisis sentimen untuk mengeksplorasi persepsi penggemar internasional berbahasa Inggris terhadap adaptasi live-action "One Piece". Dengan memanfaatkan Python Reddit API Wrapper (PRAW) dan teknik machine learning, kami menganalisis diskusi di Reddit, menggunakan algoritma Support Vector Machines (SVM) dan Random Forest untuk klasifikasi sentimen. Temuan kami mengungkapkan bahwa mayoritas komentar penggemar menyampaikan sentimen positif terhadap adaptasi live-action "One Piece", dengan 8790 komentar bernada positif dibandingkan dengan 5498 komentar bernada negatif. Model SVM dengan SGDClassifier muncul sebagai yang berkinerja terbaik, menunjukkan presisi dan skor Cohen's Kappa tertinggi di antara model yang dievaluasi. Studi ini berkontribusi pada bidang Teknologi Informasi dan studi media dengan menawarkan wawasan tentang sentimen penggemar dan akurasi metode analisis sentimen. Penelitian ini memberikan informasi untuk pengambilan keputusan bagi produser, menangani implikasi dunia nyata, dan menjembatani kesenjangan bagi pemangku kepentingan. Selain itu, penelitian ini meningkatkan pemahaman akademis tentang dinamika sentimen dalam komunitas penggemar, khususnya terkait adaptasi live-action anime. Kata Kunci: Analisis Sentimen, One Piece, Adaptasi Live-Action, Machine Learning, SVM, Random Forest
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