Text Analysis through NLP-Based Visualization

Text Analysis through NLP-Based Visualization

Author Name : Prof. Girish Dashmukhe, Akanksha Bhute, Lalita Khairnar, Vibhavari Tayade, Roshan Sanap

DOI: https://doi.org/10.56025/IJARESM.2023.1201241247

ABSTRACT In an era marked by an exponential surge in textual data, efficient text processing tools are indispensable for synthesizing insights from vast volumes of literature. This paper explores the implementation of computational methods, particularly Natural Language Processing (NLP), for Text Analytics and Visualization to address this pressing need. With a focus on the corporate world's emphasis on time efficiency, the study delves into the challenges posed by unstructured textual data within big data contexts. It presents a comprehensive analysis of a large volume of text, generating graphical visualizations such as Wordclouds and Mendenhall Curves. Additionally, the paper introduces TextSavvy, a web-based text processing application designed to streamline text analysis tasks. TextSavvy offers a suite of features including text extraction, summarization, translation, and visualization, empowered by advanced algorithms and intuitive techniques. Through its design, implementation, and evaluation, TextSavvy emerges as an effective tool for facilitating text analysis tasks and enabling informed decision-making. Comparative analysis demonstrates TextSavvy's superiority in accuracy and efficiency, owing to its integration of advanced NLP techniques and user-friendly interface design. The paper concludes by highlighting TextSavvy's broader implications in advancing text processing research and its potential to optimize time utilization across various fields, benefiting teachers, employees, students, analysts, and meeting organizers.