Sentiment and Toxicity Analysis of Tourism-Related Video through Vader, Textblob, and Perspective Model in Communalytic - Singgalen, Yerik Afrianto (Building of Informatics, Technology and Science (BITS), 2024)
This study leverages the Tourism and Travel Content Analysis (TTCA) framework to explore user sentiment and behavior in response to digital travel content. Utilizing sentiment analysis models such as VADER and TextBlob, the research analyzed 13,162 posts, revealing that 13.92% were negative, 15.02% neutral, and 71.06% positive, according to VADER. At the same time, TextBlob classified 10.47% as unfavorable, 26.51% as neutral, and 63.02% as positive. Additionally, toxicity scores calculated using Detoxify and Perspective models showed a range from low to high levels of toxic content, highlighting issues like identity attacks, insults, profanity, and threats. The findings underscore the effectiveness of well-crafted narratives in digital content for influencing tourist behavior and visit intentions. However, limitations were noted in the model's ability to fully capture emotional and cultural nuances. Future research should incorporate more advanced analytical tools and diverse datasets to overcome these limitations. Ultimately, the TTCA framework provides valuable insights for enhancing digital marketing strategies and improving user engagement in the tourism secto