Data Visualization for University Research Papers
Alpa K. Oza
Alpa K. Oza, Information Technology, Parul Institute of Engineering and Technology, Gujarat Technological University, Ahmedabad, India.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 232-235 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1163112612/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: 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: Quite many publications are being published either in form of Theses, essays or Research papers at various levels of scientists, research scholars or Ph.D students. This is a big jargon. They are required to be segregated under various Topics. Topic modeling is a set of tool that provides a solution. Topic modeling discovers a hidden thematic structure in collection of documents. Topic models are high level statistical tools. A user must scrutinize numerical distribution to understand and explore their results. Latent Dirichlet Allocation LDA has been used to generate automatically topics of text corpora and also to subdivide the corpus words among those topics. Topic models also fall in the same line of functioning. This model (topic model) has proven remarkably powerful for information retrieval tasks. Information visualization technologies when used in conjunction with data mining and text analyses tools can be of great value for various types of tasks. For this reason various visualizations have been designed. Quite laborious work has been done and still being labored at various levels of scholars. Here our aim is to present a brief description to the topical method of visualization under data mining
Keywords: Topic Models, Text Visualization, Visual analysis, Text, Statistical model