Advancing Emotion Detection in Sentiment Analysis with Concept-Level Computing and Machine Learning

Abstract

This study introduces an innovative approach to sentiment analysis and opinion mining, overcoming the limitations of traditional techniques by employing a concept-level analysis framework. Leveraging linguistic insights, common-sense reasoning, and sophisticated machine learning, the proposed method transforms unstructured text into analyzable data, resulting in a significant precision enhancement. Achieving a 92.26% precision rate, our model outperforms existing methods, particularly in parsing complex sentences with conjunctions and emotional variance. This methodology marks a significant advancement in web-based sentiment interpretation, providing an intricate, accurate, and holistic tool for analyzing online discourse and public sentiment.