Abstract
Due to ever-increasing usage of social media, people tend to excessively put up their thoughts on trending topics. Views of people about some topic can vary a lot, so Twitter posts regarding trends in India need to be analyzed. There is a need for an application which uses Twitter data to represent the opinion of public about any current topic. In this paper, an analysis on Twitter data regarding latest trends is conducted using Naïve Bayes technique which used classification and Dirichlet Multinomial Mixture (DMM) algorithm for clustering of tweets. Twitter API is used to get tweets. After preprocessing, using feature extraction feature vector list is extracted from the tweets. Now a dictionary of positive, negative, and neutral words is generated on which DMM is used for clustering. As a result, an average of 85% of the tweets was placed in the true clusters.
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Devi, J.S., Nandyala, S.P., Reddy, P.V.B. (2019). A Novel Approach for Sentiment Analysis of Public Posts. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 32. Springer, Singapore. https://doi.org/10.1007/978-981-10-8201-6_18
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DOI: https://doi.org/10.1007/978-981-10-8201-6_18
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