A Systematic and Analytical Approach to Techniques and Tools in Topic Modeling
Shanthi. S1, Nithya R2, Nagendraprabhu3
1Shanthi. S, Research Professor with Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India.
2Nithya R, Assistant Professor in the Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Affiliated to Jawaharlal Nehru Technical University, Anantapuram, India.
3Nagendraprabhu, Professor, Department of Computer Science and Engineering, Malla Reddy College of Engineering & Technlogy, Dhulapally, Secunderabad, India

Manuscript received on 21 April 2019 | Revised Manuscript received on 26 May 2019 | Manuscript published on 30 May 2019 | PP: 2932-2935 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1331058119/19©BEIESP
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© 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: Topic modeling is one of the recent upcoming research areas of interest among the researchers. Topic Modeling is a straightforward way to examine the huge volumes of unstructured data. Each topic is a collection of words and these words usually bond together more frequently. When this technique is applied to a huge volume of data it can join words having same meanings and distinguish the uses of words with multiple meanings. The intention is to study and examine different topic modeling algorithms and to perform a brief literature review and analysis was performed and the obtained results are presented in this paper. Many techniques for topic modeling proposed by different researchers are put together and characteristics and drawbacks of various techniques have discussed. We present this paper with the intention that it will help few of the researchers in finding out the problems, present challenges and future scope of research in topic modeling.
Index Terms: Topic Modeling, Topic, Words, LDA, Supervised, Unsupervised

Scope of the Article: Emergent Topics