Abstract: The proliferation of user-generated content on social media has made opinion mining an arduous job. As a micro-blogging platform, Twitter is being used to collect views about products, trends, and politics. Sentiment analysis is a technique used to analyse the attitude, emotions, and opinions of different people towards anything, and it can be carried out on tweets to analyse public opinion on news, policies, social movements, and personalities. By employing Machine Learning models, opinion mining can be performed without reading tweets manually. Their results could assist governments and businesses in rolling out policies, products, and events. Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. With an in-depth comparative performance analysis, it was observed that the proposed voting classifier (LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score. To further validate the stability of the proposed approach on two more datasets, one binary, and another multi-class dataset, and achieved robust results.
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DOI:
10.17148/IJARCCE.2022.111010
