Comparing the Performance of Machine Learning Algorithms for Emotion Classification on Tweets

The rapid increase in the availability of textual content due to Industry Revolution 4.0 has made sentiment analysis an important area of machine learning research . This study aims to develop a mechanism to identify the hidden emotions in textual content, beyond the three basic sentiments of positive , neutral , and negative . Several machine learning approaches to emotion classification , including Naive Bayes classifiers , Support Vector Machines , Regression , Decision Trees , and Random Forests have been explored . The experiments show that simple linear models can achieve high accuracy (up to 90.5%), suggesting that complex algorithms are not always necessary for effective emotion classification . The performance of the models was evaluated using a variety of metrics , including accuracy , precision , recall , F-score and efficiency . The findings suggest that machine learning approaches can be used to effectively identify emotions in textual content, even with simplemodels. This has potential applications in a variety of domains , such as social media analysis , customer service, and healthcare.