Deep Emotion Analysis: “Enhancing Emotion classification with Transformer Model”

In the era of digital technology, it becomes very critical to perceive human emotions in textual data from the point of view of many applications - sentiment analysis, mental health monitoring and user experience optimization. The investigation is on the incorporation of transformer-based models and ML techniques in order to enhance the accuracy for emotion classification. This work employs a variant of Bidirectional Encoder Representations from Transformers (BERT) that is able to preserve complex linguistic constructs and contexts. Along with this, logistic regression, decision tree, support vector machine (SVM), random forest, and gradient boosting algorithms are utilized to classify emotional aspects which are then compared for their accuracy in expressing subtleties present in text data. Fundamentally, eXplainable AI (XAI) tools such as ELI5 for random forest and LIME for BERT are employed to improve the interpretability and transparency of the emotion categorization process. These XAI tools enable users to see the feature importance and decision-making process of the model which helps in improving their understanding of the critical features that influence emotion classification. This work proposes the integration of transformer models and traditional machine learning algorithms with explanation of sentiments (XAI) to boost text-based emotion analysis.