A Neutrosophy Framework for Emotion Analysis in Audio and Text Files using Harris Hawk Optimization

Abstract In the digital data landscape, businesses are increasingly reliant on advanced tools for extracting actionable insights, with sentiment analysis being at the forefront. This study breaks new ground by exploring sentiment analysis in audio data through the lens of Neutrosophy. Our approach hinges on Harris Hawk Optimization (HHO) augmented with Neutrosophic Sets for efficient feature selection. Utilizing the Libri TTS train clean 100 dataset, our experiments demonstrate the superiority of our model in reducing feature numbers while enhancing sentiment analysis accuracy. Remarkably, the Neutrosophic Sets significantly boost HHO's performance, achieving a best fitness of 0.96, and enabling the selection of a minimal number of features (39 out of 300) with a rapid convergence rate. Classification accuracy, using SVM, KNN, and DT classifiers, shows a notable improvement over other fitness functions, particularly with Neutrosophic Sets, where accuracy increases to 0.96. Further, our model outperforms baseline algorithms in sentiment classification, with an 18\% error rate reduction compared to the nearest competitor. Clustering analysis of the LibriSpeech dataset using Single-Valued Neutrosophic Sets reveals insightful patterns, underlining the model's potential to transform sentiment analysis by integrating both audio and textual data. This research not only advances sentiment analysis techniques but also pioneers the application of Neutrosophy in audio data analysis, offering novel insights and a robust framework for businesses and researchers in the digital era.