Abstract
In this present age, everyone uses social media. Whilst ideas spread through these mediums can be colourful, there are certain views of people which are shared with a tone of negativity, mischief and offensiveness to them as well. One of the most used of such social media platforms include Twitter, which is a platform where messages with limited word limit are sent by the people. This present work, thus, is about analysing the polarity of a tweet by using millions of data achieved through Natural Language Processing (NLP). We collected a large dataset of tweets which are marked by levels of polarity from negative to positive. We use Machine Learning algorithms on this dataset to detect the polarity of the tweet. Here we have used Multinomial Naïve-Bayes, Complement Naïve-Bayes and Logistic Regression classifier to find the polarity of tweets. The dataset size is 1.6 million and we got the best result using Logistic Regression. The highest accuracy is 78.05%.
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Rakshit, P., Gupta, S., Das, T. (2023). Sentiment Analysis to Find Sentence Polarity on Tweet Data. In: Deva Sarma, H.K., Piuri, V., Pujari, A.K. (eds) Machine Learning in Information and Communication Technology . Lecture Notes in Networks and Systems, vol 498. Springer, Singapore. https://doi.org/10.1007/978-981-19-5090-2_19
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DOI: https://doi.org/10.1007/978-981-19-5090-2_19
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