Implementation of the GloVe in Topic Analysis based on Vader and TextBlob Sentiment Classification - Singgalen, Yerik Afrianto (Building of Informatics, Technology and Science (BITS), 2024)
This research investigates public sentiment towards tourism and gastronomy content through sentiment classification methodologies, employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Leveraging sentiment analysis techniques, including Vader and TextBlob, the study analyzes a dataset of textual content related to tourism and gastronomy to discern prevailing sentiment distributions. The findings reveal a predominant prevalence of positive sentiments (72.19%), followed by neutral (23.33%) and negative sentiments (4.48%). These results shed light on the overall sentiment dynamics surrounding tourism and gastronomy content, indicating a predominantly positive reception among users. The study contributes to the body of knowledge in sentiment analysis research, particularly within tourism and gastronomy studies, offering valuable insights into user perceptions and attitudes. Such findings have implications for content creators, marketers, and policymakers seeking to enhance tourism and gastronomy experiences. Future research could delve deeper into the factors influencing sentiment expressions and explore strategies to leverage positive sentiments for promoting and advancing tourism and gastronomy endeavors within the CRISP-DM framework.