Emotion Prediction Using Natural Language Processing: A Performance Evaluation of Supervised Machine Learning Models for Classification Tasks

This study discusses the usage of machine learning algorithms in the detection of emotions. It helps to fill the gap between the human understanding of emotions and the artificial intelligence being developed, both in relation to technological progress and human-machine interactions. Applying the KDD process to NLP systematically involves identifying, preprocessing, mining patterns, and interpreting textual data. Techniques of Data Mining, like Logistic Regression, Linear SVC, and Multinomial Naïve Bayes, have been applied for the purpose, accompanied by metrics like Classification Reports for Precision, Recall, F1-score, and Accuracy and k-fold cross-validation for a very robust and accurate analysis of the unstructured text domain. Logistic Regression remained the most scoring model of 90%. Its score now was a few percentage points smaller than its training score. As expected, this time Linear SVC found itself in second place with an 88%, and Multinomial Naïve Bayes also stayed in third place on 86%. With cross-validation applied, Logistic Regression proves to be the most reliable model for this NLP study. It scores high and is quite generalizable and stable, thus suitable for deployment in scenarios where robustness and consistency are essential. Potential further improvements to such models could involve hyperparameter and fine-tuning. By trying them out on much larger and varied datasets, they can be put through several checks for testing capabilities and limits within applications. One other extension involves ensemble techniques to build even stronger, more reliable solutions by merging different model strengths together.

Logistic Regression, Linear SVC, Multinomial Naïve Bayes, Machine Learning, Natural Language Processing

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