Enhancing Emotion Detection in Textual Data: A Comparative Analysis of Machine Learning Models and Feature Extraction Techniques

The digital age has resulted in a massive increase in the amount of available textual data, including articles, comments, texts, and updates on social networks. The value of analyzing such a large volume of data extends to many other industries and applications, as it provides important insights into the perspectives of customers, strategic decision-making, and market demands. Detecting emotions in texts faces challenges due to linguistic patterns and cultural nuances. This study proposes a system capable of accurately identifying emotions expressed in text using a variety of machine learning models, including logistic regression, extra randomized tree, voting, SGD, and LinearSVC. It also employs different feature extraction techniques, such as TF-IDF, Bag-of-Words, and N-grams, comparing their performance in these models. An evaluation was carried out using two English emotion datasets, namely ISEAR and AIT-2018, using F1 score, accuracy, recall, and precision. The findings demonstrate the ability and effectiveness of the system to detect emotions conveyed within texts. The LinearSVC model with N-grams achieved the highest accuracy of 88.63% on the ISEAR dataset, while the extra randomized tree classifier with N-grams achieved 89.14% accuracy on the AIT-2018 dataset. Furthermore, the SGD model with TF-IDF achieved 88.18% and 84.54% accuracy on the ISEAR and the AIT-2018 datasets, respectively.