Application Design Location Search Recommendations And Sentiment Analysis Using Text Mining
DOI:
https://doi.org/10.71302/jbidai.v7i2.66Keywords:
Text Mining, Attraction Place, Culinary, Naïve Bayes, Sentiment AnalysisAbstract
This research aims to provide information and assessments about tourist attractions and culinary attractions that are popular on social media (Instagram and Twitter). The research process uses a text mining approach, starting with text processing (case folding, tokenizing, stopword removal, and stemming) to filter comments. Furthermore, weighting is carried out using the TF-IDF method to determine the relevance of words. The process of classifying comments by location name is carried out using the Naïve Bayes algorithm, followed by sentiment analysis to assess positive, negative, or neutral comments. The research application was built using PHP with a MySQL database and utilized a dataset of 73 comments (17 for tourism and 56 for culinary) collected from social media. The results of the study show that the system is able to produce recommendations for tourist and culinary attractions effectively based on data analysis
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