Abstract
The purpose of our research paper is to discover the best method to implement opinion mining on Ukraine–Russian war based on the tweets generated by people all over the world on Twitter. We have performed VADER, Naive Bayes, and DistilBERT and have done some relative comparative studies finalizing that VADER is the best approach to perform opinion mining for a vast data like Ukraine–Russian war because VADER gives high accuracy based on emotions just like a human, who understands contexts where machines cannot. We choose DistilBERT and Naive Bayes as they are some of the popular models which give the best output based on sentiment score hence, we figured out that the best model which is optimal to find the sentiment accuracy based on the emotion is VADER when compared with DistilBERT and Naive Bayes. The two main aspects which highly differentiate VADER from all other methods are Polarity and Intensity. VADER being a library has a powerful set of modifiers making it more unique which gives higher accuracy to perform opinion mining on Ukraine–Russian war tweets.
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Anudeepthi, D., Vutla, G., Bhargavi Reddy, V.R., Santhi Sri, T. (2023). Opinion Mining on Ukraine–Russian War Using VADER. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-99-5085-0_19
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DOI: https://doi.org/10.1007/978-981-99-5085-0_19
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