Abstract
Twitter is a microblogging site that helps in opinion mining of tweets (reviews) from people [1]. Understanding the reviews from their tweets helps in determining what a business needs to improve on. This paper focuses on the algorithms used that are SVM and Naïve Bayes, their functionality, accuracy, precision and recall value. The effect of the two machine learning algorithms gives us the idea of which algorithm can fetch the best suitable results. To determine this, we use a medium to understand it which is ‘confusion matrix’. The datasets used are ‘Uber’ and ‘Ola’. Uber and Ola have a large number of users who use the cab service regularly. We extract tweets that vary in number and the algorithms check the words which represent sentiments of people’s review. The algorithms then train the data from the tweets to identify which tweets can be categorized as positive, negative and neutral.
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Anthal, J., Upadhyay, A., Patil, A., Indulkar, Y. (2021). Effects of Uber and Ola on SVM and Naïve Bayes. In: Goyal, D., Gupta, A.K., Piuri, V., Ganzha, M., Paprzycki, M. (eds) Proceedings of the Second International Conference on Information Management and Machine Intelligence. Lecture Notes in Networks and Systems, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-15-9689-6_53
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DOI: https://doi.org/10.1007/978-981-15-9689-6_53
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