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Analisis Text Clustering Masyarakat Di Twitter Mengenai Omnibus Law Menggunakan Orange Data Mining
Article
Article
Analisis Text Clustering Masyarakat Di Twitter Mengenai Omnibus Law Menggunakan Orange Data Mining
Ratu aghnia raffaidy wiguna et al · Asosiasi Doktor Sistem Informasi Indonesia · 2021 · ISSN 2656-5935
Sosial media dengan platform twitter menjadi hal menarik untuk diteliti. Trending topik tersebut menghasilkan komentar masyarakat Indonesia yang mengandung opini berupa emosi. Penelitian ini mencoba menganalisis opini di...
LCC LCC:Mathematics; LCC:Electronic computers. Computer scienceIdioma English
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Open Access
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Frames and sentiments of the Twitter communication by German Chancellor Scholz during the Russian invasion of Ukraine
Article
Article
Frames and sentiments of the Twitter communication by German Chancellor Scholz during the Russian invasion of Ukraine
Nisch Stefan · Metropolitan University Prague / Central European Political Science Association · 2023 · ISSN 2787-9038
In a complex information environment, Russia’s invasion of Ukraine presents a major challenge to the communication of political leaders throughout the world. The objective of this article is to analyse the frames and s...
LCC LCC:Political scienceIdioma English
Acceso abiertoRuta libre sin proxy. Acceso recomendado cuando no hay suscripción activa.
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Hybrid Sentiment Analysis Model for Customer Feedback Interpretation Using Lexicon, Machine Learning and Deep Learning Techniques
Article
Article
Hybrid Sentiment Analysis Model for Customer Feedback Interpretation Using Lexicon, Machine Learning and Deep Learning Techniques
Jou Jia Yi et al · MMU Press · 2026 · ISSN 2821-370X
Customer feedback is pivotal in enhancing service quality and user satisfaction across digital platforms. However, traditional sentiment analysis methods often struggle with informal languages, contextual nuances, and as...
LCC LCC:Electronic computers. Computer science; TENDOkluZm9ybWF0aW9uIHRlY2hub2xvZ3k~Idioma English
Acceso abiertoRuta libre sin proxy. Acceso recomendado cuando no hay suscripción activa.
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