Implementation of Perspective, Vader, and TextBlob in Toxicity and Sentiment Analysis of Food and Tourism
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Yerik Afrianto Singgalen
*
Atma Jaya Catholic University of Indonesia, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
This research investigates the sentiment and toxicity of viewer responses to digital content on food and tourism using the Digital Content Reviews and Analysis Framework. Employing advanced text processing and sentiment analysis models such as Perspective, TextBlob, and Vader, the study analyzed 4,166 comments. The findings reveal a predominantly positive viewer sentiment, with VADER identifying 18.98% negative, 21.33% neutral, and 59.69% positive sentiments, while TextBlob identified 14.42% negative, 33.86% neutral, and 51.72% positive sentiments. The toxicity analysis highlighted various levels, with an average toxicity score of 0.15783 and notable scores for severe toxicity, identity attack, insult, profanity, and threat. The research underscores the importance of comprehensive sentiment analysis in understanding viewer engagement, providing valuable insights for content creators and marketers in the tourism industry. The study concludes with recommendations for further exploration and refinement of sentiment analysis methodologies to enhance the understanding and management of digital content interactions.
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