Abstract
Sentiment classification is the task of categorizing the text into different opinionated categories positive, negative and neutral depending upon the user’s post on social media in a particular domain. In social media, Twitter is the most popular platform for classification but with the limitations of number of words to be posted by individual which produces the inaccurate classification of dataset. Hence, in this paper, we are trying to increase the performance of the sentiment classification model by collecting the textual data on the same domain from different sources. To validate the same, different state-of-the-art machine learning classification algorithms have been applied and analyzed that shows the better results for hybrid data in comparison with the single-source Twitter (microblog) data and also articulated the most suitable algorithms for the hybrid textual sources of data.
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Arya, V., Agrawal, R. (2021). Comparative Analysis of Machine Learning Algorithms for Hybrid Sources of Textual Data: In Development of Domain Adaptable Sentiment Analysis Model. In: Kumar, R., Quang, N.H., Kumar Solanki, V., Cardona, M., Pattnaik, P.K. (eds) Research in Intelligent and Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-7527-3_16
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