Analisis Sentimen Pada Data Ulasan Hotel Menggunakan Metode Naïve Bayes Classifier

Safitri, Nadea Ajeng (2023) Analisis Sentimen Pada Data Ulasan Hotel Menggunakan Metode Naïve Bayes Classifier. Diploma thesis, Politeknik Negeri Jember.

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Abstract

Traveloka has become a popular platform for users to share their experiences and provide opinions and sentiments about the hotel services they have used. In this study, the researchers utilized hotel review data from the Traveloka platform to analyze public sentiment using the Naïve Bayes Classifier method. The preprocessing and TF-IDF stages were used to process the text data from hotel reviews so that it could be used as input for the Naïve Bayes classification model. The analysis results showed that the Naïve Bayes Classifier method could predict public sentiment in hotel review data with a fairly high accuracy, providing useful information for hotel owners to improve the quality of their services and provide a better experience for users. This research showed that sentiment analysis on hotel review data using the Naïve Bayes Classifier method can produce accurate results, with an accuracy of 89%, precision of 90%, and recall of 89%. These analysis results can provide valuable insights for hotel owners and managers to understand their customers' opinions and sentiments and make appropriate improvements. By analyzing hotel review data on social media platforms like Traveloka, businesses can gain insights into their customers' experiences and adapt their services to meet their needs and preferences. Furthermore, the use of the Naïve Bayes Classifier method in hotel review sentiment analysis also holds potential for further development in the future. The use of smarter algorithms and more sophisticated pre-processing techniques can improve the accuracy of sentiment analysis results, providing more useful insights for hotel owners and managers to improve their service quality. Additionally, further development in sentiment analysis techniques can help businesses better understand their customers' preferences and needs, ultimately increasing customer satisfaction and business success. Therefore, sentiment analysis on hotel review data can be a highly useful tool for businesses to gain valuable insights into improving their services and better meeting their customers' needs. Keywords : Traveloka, Sentiment Analysis, Naïve Bayes Classifier

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