Exploring Algorithmic Approaches for Emotion Classification in Text

The classification of emotions in text is a rapidly growing field with significant applications, including sentiment analysis and social media monitoring. However, achieving high accuracy in emotion classification remains a challenge. This research aims to explore algorithmic approaches for emotion classification using the ISEAR dataset, which includes various emotions such as joy, fear, anger, sadness, disgust, shame, and guilt. Researchers employ machine learning techniques such as Random Forest, Logistic Regression, Naive Bayes, and Support Vector Machines (SVM), and use data augmentation techniques to improve model performance. Augmentation methods, including synonym replacement and random embedding, are used to increase the diversity of the training data. The dataset is divided into training and testing sets with a ratio of 80:20. The results show that Random Forest excels with the highest score on all performance metrics: Accuracy, Precision, Recall, and F1-Score of 92% each. SVM also shows good performance, especially in Recall, with a score of 79%. Logistic Regression has stable performance across all metrics with scores of 76%. The Naive Bayes model demonstrates the poorest performance, achieving an accuracy rate of 69%, precision rate of 75%, recall rate of 69%, and F1-Score of 69%. In conclusion, data augmentation techniques significantly enhance model performance, with the Random Forest model demonstrating the highest efficiency in emotion classification in this study.