Atharv Gyan

Reimagining Natural Language Processing: Tracing the Journey from Rigid Rule‐Based Systems to Dynamic AI‐Driven Innovations Introduction Natural Language Processing (NLP) stands at the fascinating crossroads of computer science, linguistics, and artificial intelligence a discipline that has undergone a remarkable evolution over the past several decades. Initially conceived through the lens of rule‐based systems, NLP was once governed by a rigid structure of hand‐crafted rules and predefined patterns. These early systems, while pioneering in their own right, were limited by their inflexibility and inability to truly capture the nuances of human language. Today, however, NLP has blossomed into a vibrant field powered by dynamic, AI-driven innovations. Modern techniques based on machine learning and deep learning have redefined what computers can understand, generate, and accomplish with language. This transformation is not merely a technical upgrade but represents a profound shift in...

Sentiment analysis on multimodal data refers to the process of analyzing and understanding emotions expressed across various types of data, such as text, images, and audio. This approach integrates multiple modalities of data to gain a more comprehensive understanding of sentiment and emotion. Multimodal Data: Multimodal data refers to data that is represented in multiple forms or modes, such as text, images, and audio. Each modality provides unique information that can contribute to understanding sentiment and emotions. Sentiment Analysis: Sentiment analysis, also known as opinion mining, is the process of identifying and extracting subjective information from textual data. It involves determining the sentiment expressed in a piece of text, whether it is positive, negative, or neutral. Emotion Detection: Emotion detection goes beyond sentiment analysis by aiming to identify specific emotions expressed in the data. This can include emotions such as happiness, sadness, anger, and more n...