9th International Conference on ICT for Sustainable Development

Authors - Priti T. Chorade, Preeti D.Chandanshive, Sneha Deepak Shinde, Vaishnavi Biradar, Mrunali Gaikwad
Abstract - Text analysis involves uncovering and extracting valuable insights from unstructured text data. It spans various tasks such as information retrieval (e.g., retrieving reports or website content), text classification, clustering, and more recently, entity, relation, and event extraction. Natural Language Processing (NLP) aims to derive comprehensive meaning from free text, essentially deciphering who did what to whom, when, where, how, and why. NLP relies on linguistic principles like part-of-speech tagging and grammatical structure analysis. As the volume of data continues to grow annually, there is a pressing need to synthesize and extract insights from vast amounts of literature. Text analysis and visualization are crucial for effective data interpretation, offering users the ability to comprehend information within constraints. In the corporate world, time is valued more than money, making quick comprehension of data essential for presentations and decision-making. This paper presents a case study on the application of computational methods, particularly Natural Language Processing (NLP), for text analytics and visualization using relevant libraries. At any moment the worker is instructed to provide presentation and it takes hundreds of time to apprehend what data is, for what it turned into created, what is cause the entirety has to be recognize. Natural language processing provides a very significant contribution to various application areas such as multilingual big data, information retrieval, data integration and multilingual web. However, handling linguistic knowledge to develop such lingware applications is a crucial issue, especially for linguistic novice users. To deal with this issue, a "smart" linguistic knowledge management may help the users to understand the meaning, scope and especially the use of related techniques and algorithms. we propose a semantic processing of linguistic knowledge based on a multilingual linguistic domain ontology, called LingOnto. Compared to related work, LingOnto does not only handles linguistic data, but also linguistic processing functionalities and linguistic processing features.