Abstract
In the present scenario, sentiment analysis has gained much attention in the field of text mining. As social media have a huge impact on one’s life, people use social media as a tool to express their feelings, thoughts, opinions, emotions and ideology. With the help of sentiment analysis, we can provide computational treatment for sentiments, opinions and subjectivity of text. Detecting spams from various social media sites is a challenging task as the messages contain the short informal text. In this paper, we have tested different classifiers on spam data of users’ tweets based on spammer and non-spammers. The classifiers used for the purpose are Naives Bayes, Simple Logistic, J48 pruned tree, Bayes network classifier and Random Forest. Analysis results showed that random forest is proving the highest accuracy among all the algorithms under consideration.
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Kumar, S., Kumar, R., Sidhu (2022). Comparative Analysis of Classifiers Based on Spam Data in Twitter Sentiments. In: Tomar, A., Malik, H., Kumar, P., Iqbal, A. (eds) Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-16-2354-7_36
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DOI: https://doi.org/10.1007/978-981-16-2354-7_36
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