Unlocking Emotions in Text: A Comprehensive Study of Computational Linguistics and Natural Language Processing Techniques (Journal of Advances and Scholarly Researches in Allied Education, 2023)
The widespread use of digital communication has led to the creation of vast amounts of written content every day. Understanding the emotions expressed in this data is crucial for the development of emotionally intelligent computer systems. This article presents the development of a novel approach, known as CLBEDC-SND, which aims to recognise and classify Social networking data containing sentiments. Numerous phases of data pre-processing were performed on the CLBEDC-SND model to guarantee its suitability for subsequent processing. The CLBEDC-SND model undergoes sentiment scoring and vectorization utilising a fuzzy methodology. Vectorization is the conversion of textual information into a vector representation, which may be used with the ELM model.  The ELM model was utilised by the CLBEDC-SND model to provide accurate and timely emotion categorization. The procedure selects the input weight in a random manner and computes the output weight of the Single Layer Feedforward Network (SLFN) using empirical data. Subsequently, the starting weight of the input and the bias of the hidden layer are chosen at random.  Using the SFLO method, the parameters of the ELM model are optimised in the final stage. The study data demonstrated that the CLBEDC-SND method consistently yielded enhanced outcomes across all domains. A comprehensive comparison analysis will be done to improve the precision of emotion categorization results produced from the CLBEDC-SND model. The experimental evaluations have demonstrated that the CLBEDC-SND model outperforms alternative models in its ability to classify emotions. The enhanced performance of the CLBEDC-SND model can be ascribed to the integration of sentiment scoring based on fuzzy logic and the optimum parameter modification strategy based on SFLO. Thus, the proposed model is applicable.