Enhancing Multi-Emotion Detection in Text: A Comparative Study of Feature Extraction Techniques and Machine Learning Models

Emotion detection in text is one of the major tasks in NLP that might have tremendous applications in sentiment analysis, human-computer interaction, and mental health diagnosis. This study evaluates the performance of various feature extraction techniques and classification models for multi-emotion detection, using a dataset of 15,996 samples divided with six emotion labels: Sadness, Joy, Love, Anger, Fear, and Surprise. Three feature extraction methods—TF-IDF, Count Vectorization, and N-grams—were applied to preprocess the text data. Six models—KNN, SVM, NB, DT, LR, and BiLSTM—were then used to classify emotions. The best results among the 18 feature-model combinations are TF-IDF with SVM at 86.15%, Count Vectorization with Logistic Regression at 86.25%, and N-grams with SVM at 86.06%. N-grams combined with LR also performed well, achieving an accuracy of 88.09%, underlining the importance of capturing contextual and sequential information in text classification. This research contributes to improving NLP-based emotion detection systems and provides insights for future work, particularly in domains requiring nuanced emotion recognition from text data.