Abstract
With the advent of the significant data era, people are confronted with the vast amount of information they receive each day. The quantity of information accrued and processed by Facebook, Twitter, and other significant social networks (such as Instagram) is vast. The Twitter platform encourages users to use 280 characters each to tweet their thoughts. Because tweets can use a limited number of characters, sentiment analysis becomes more accurate. Sentiment analysis is a technique for determining whether a text is positively, negatively, or neutral. Some experiments are conducted using Natural Language Processing Toolkit (NLTK) to determine whether a tweet has a neutral, positive, or negative polarity with accuracy. Moreover, by using Naïve Bayes and SVM, the accuracy of the tweets is compared. Finally, the ROC curve will decide the efficiency of both algorithms.
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Kiran Kumar, P., Jahna Tejaswi, N., Vasanthi, M.L., Srihitha, L.L., Phanindra Kumar, B. (2022). Sentimental Analysis on Multi-domain Sentiment Dataset Using SVM and Naive Bayes Algorithm. In: Garg, D., Jagannathan, S., Gupta, A., Garg, L., Gupta, S. (eds) Advanced Computing. IACC 2021. Communications in Computer and Information Science, vol 1528. Springer, Cham. https://doi.org/10.1007/978-3-030-95502-1_16
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DOI: https://doi.org/10.1007/978-3-030-95502-1_16
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