MCWA-LSTM with SELU for Text-Based Emotion Classification
Bhargavi Vemala, M. Humera Khanam · Ingénierie des systèmes d information · 2025
Text-based emotion classification involves determining text to categorize emotions like sadness, anger, fear, happy, and so on.It employs Natural Processing Language (NLP) techniques for understanding sentiment and emotional tone behind words.This technique is widely employed in social media, customer feedback analysis, etc., However, accurately classifying emotions from text remains challenging because of sarcasm, ambiguity, and contextual nuances of human language leads to incorrect emotional responses.This research proposes Monotonic Chunk Wise Attention Long Short-term Memory with Scaled Exponential Linear Unit (MCWA-LSTM with SELU) for mulri-label text based emotion classification.In traditional LSTM, MCWA is incorporated to focus on relevant chunks of input sequentially which minimize noise from irrelevant parts and captures significant context effectively.LSTM capture long-term dependencies and contextual information which makes effective for emotion classification whereas SELU improves learning by managing self-normalizing properties that enhance training and model stability.Therefore, MCWA-LSTM with SELU achieves high accuracy of 98.66%, 98.32% on SemEval-2018 Task1-C, GoEmotion datasets for multi-class which is 37.46% and 27.12% higher compared to Universal Conceptual Cognitive Annotation-Graph Attention Network (UCCA-GAT).The proposed method obtains high f1-score of 98.21% for binary classification on TEL-NLP dataset which is 15.21% higher than existing Bidirectional Encoder Representations from Transformer (BERT) and Clipped Asymmetric Loss (ASL).