Efficient Input File Classification by Applying Natural Language Processing and Hybrid Deep Learning Technique

Abstract

The digital era's advancements have prompted the adoption of communication as the primary medium for the corporate industry. Formerly, business discussions, profiles, conferences, purchasing, and settlements were all carried out in person. But the modern era has made everything digitized. In the past few years, it's been observed there is an exponential increase in the count of complicated manuscripts and writings that need a better recognizing of machine learning methodologies to successfully detect languages in various purposes. Several Artificial Intelligent approaches have produced outstanding achievements in processing natural languages. The ability of various machine learning and deep learning to realize complex models and non-linear associations within data is critical to their efficiency. Learning applicable frameworks, architecture, and algorithms for input classification, such as text files, audio, and video files, on the other hand, is a challenging task. Objective: This study aims at Natural Language Processing in the identification of text, voice messages, smart records, and chatbots. Hybrid deep learning approach for the classification of the inputs that are in the form of text, voice, and video records. Problem Statement: As interaction becomes more crucial to business, firms have designed sophisticated NLP programs. These NLP take human wishes and satisfy them quickly through messages, telephone calls, digital records, and chatbots. The ease of communication and connection has shown a stronger impact on customer preferences, aspirations, and demands. Contemporary service providers today utilize email, messaging, telephone calls, digital records, and chatbots as primary points of contact for practically all of their transactions, client inquiries, and preferable trade channels. Method: The study uses text content, voice message, and audio as part of Natural Language Processing and Hybrid Deep Learning approaches to demonstrate how input is processed depending on user reactions, replies to text messages, and audio record identification during communications.