Enhanced Text Emotion Prediction Algorithm: A Comparative Study with Support Vector Machines for Emotion Prediction in Text
International Research Journal of Modernization in Engineering Technology and Science · 2023
Text emotion prediction is a critical task in various applications, including sentiment analysis, customer feedback analysis, and social media monitoring.This paper presents a comparative study between the Enhanced Text Emotion Prediction (ETEP) algorithm and the Support Vector Machines (SVM) algorithm for text emotion prediction.The ETEP algorithm incorporates data preprocessing, feature extraction, and machine learning techniques to predict emotions from textual data.SVM, a widely used machine learning method, is employed as a benchmark for comparison.A comprehensive evaluation is conducted using a suitable dataset, and performance metrics such as accuracy, precision, recall, and F1-score are utilized to compare the two algorithms.Experimental results demonstrate that the ETEP algorithm achieves competitive performance compared to SVM, highlighting its effectiveness in text emotion prediction.The proposed algorithm successfully captures emotional cues by leveraging appropriate preprocessing techniques, feature extraction methods, and machine learning models.The findings of this study provide valuable insights into the development of accurate and efficient algorithms for text emotion prediction, contributing to advancements in natural language processing and sentiment analysis.The ETEP algorithm's ability to accurately predict emotions from textual data has significant implications for improving user experience, sentiment analysis applications, and decisionmaking processes based on emotional responses.Further research can explore the algorithm's scalability and adaptability to larger and more diverse datasets to enhance its practical applicability in real-world scenarios.