Abstract
Twitter is a social media forum that permits individuals to share and express their perspectives regarding the matter and post messages. A great deal of examination has been done on the sentiment analysis of Twitter data. Our proposed methodology aims to incorporate the techniques of sentimental analysis, assessing the polarity of the tweets, and improving the accuracy of the model. This paper includes classifying of tweets into sentiments: positive and negative. For this paper, we have used different data preprocessing techniques for cleaning up the data by eliminating stop word, slangs, emoticons, and hashtags. To build the productivity and the precision of the model, we have used term frequency inverse data frequency, count vectorizer, stemming, and N-gram features. Several techniques have been used currently for sentiment analysis which is discussed in brief in this paper. Out of which, we have used supervised learning approach and compared different algorithms such as Naive Bayes, support vector machines, linear regression, decision tree, XGBoost, and random forest.
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Brijpuriya, S., Rajalakshmi, M. (2022). Deployment of Sentiment Analysis of Tweets Using Various Classifiers. In: Manogaran, G., Shanthini, A., Vadivu, G. (eds) Proceedings of International Conference on Deep Learning, Computing and Intelligence. Advances in Intelligent Systems and Computing, vol 1396. Springer, Singapore. https://doi.org/10.1007/978-981-16-5652-1_14
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