Abstract
Sentiment analysis is gaining popularity due to requirements to understand the opinions expressed by users. Even in the tourism industry, the applicability of sentiment analysis is vital. Many tourists express their opinions in terms of reviews on social media. The main motivation for analyzing tourist review data is to improve the services and their stay. Also, many prominent tourists would influence by opinions expressed by early tourists. In this research, the tourist review data which is collected from various social media is analyzed. The sentiment classification is then carried out using feature method term frequency-inverse document frequency (TF-IDF) and classifiers. On hold out the evaluation method, the classification rate of 80.0 and 74.0% observed for Support Vector Machines (SVM) and Naïve Bayes classifier, respectively.
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Patel, J., Urolagin, S. (2021). Sentiment Analysis and Prediction of Point of Interest-Based Visitors’ Review. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_36
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DOI: https://doi.org/10.1007/978-981-15-5243-4_36
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