Marathi Text Analysis using Unsupervised Learning and Word Cloud
Prafulla B. Bafna1, Jatinder kumar2, R. Saini3
1Prafulla B. Bafna*, Research, Symbiosis Institute of Computer Studies and Symbiosis International Deemed University-Maharashtra, India.
2Jatinder kumar, Research, Symbiosis Institute of Computer Studies and Symbiosis International Deemed University-Maharashtra, India.
3R. Saini, Research, Symbiosis Institute of Computer Studies and Symbiosis International Deemed University-Maharashtra, India.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 338-343 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C4727029320/2020©BEIESP | DOI: 10.35940/ijeat.C4727.029320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Managing a large number of textual documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. Marathi is one of the oldest of the regional languages in the Indo-Aryan language family, dating from about AD 1000. Abundance of Marathi literature has generated a big corpus and need of summarization of information. The objective of this study is to overcome the scalability problem while managing the documents and summarize the Marathi corpus by extracting tokens. The work is better in terms of scalability and supports the consistent quality of cluster for incremental data set. Most of the past and contemporary research works have targeted English corpus document management. Marathi corpus has been mostly exploited by the researchers for exploring stemming, single-document summarization and classifier design on Marathi corpus. Implementing unsupervised learning on the Marathi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different datasets and results prove the robustness of the proposed approach for Marathi Corpus.
Keywords: Classification, Clustering, Document Management, Marathi, Summarization, Word Cloud.