Exploring WordNet® graphs for text summarization and sentiment analysis in Bengali speech

Abstract

Bengali is a complex and colorful language whose varied dialects and intonations present special difficulties for voice recognition. To better understand the subtleties of the Bengali language, this research study investigates the creation of strong natural language processing (NLP) tools. Specifically, it focuses on sentiment analysis, speech-to-text recognition, and WordNet® graph integration. It looks into Bengali-specific text summarizing strategies that can condense large amounts of material while keeping important details. Novel approaches are proposed to address the linguistic nuances of Bengali text by utilizing machine learning and natural language processing techniques. The usefulness of these methods is illustrated through empirical assessments and case studies, advancing text summarization in the Bengali language. Although there have been substantial advances in each of these fields separately, there is a clear research deficit in the area of complete solutions that deal with the particular difficulties raised by Bengali speech. This report suggests innovative approaches to close this gap. The usefulness and relevance of the suggested approaches are illustrated through empirical assessments and case studies, opening the door for future developments in Bengali natural language processing and adding to the larger field of multilingual NLP research.