Abstract
With the development in Information technology and advancement in education and research, a huge number of research publications and articles are generated every year. Crucial knowledge and information about innovative technology are embedded in these documents. It has become necessary to find a text analytics technique that gives quick insight into the research content from such enormous unstructured text data. Here we proposed information retrieval technique using topic modeling for taking quick outlook of the data. Latent Dirichlet is a generative probabilistic model which uses probability distribution of words in document to extract theme of documents. Topics generated show many hidden themes, correlated terminologies to main theme of documents which gives a quick overview of these documents. In this work Blockchain Technology, emergent technology publications are considered.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blei, D.M., Andrew, Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chan, K.R., Lou, X., Karaletsos, T., Crosbie, C., Gardos, S., Artz, D., Rätsch, G.: An empirical analysis of topic modeling for mining cancer clinical notes. In: 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 56–63 (2013). https://doi.org/10.1109/icdmw.2013.91
O’Neill, J., Robin, C., O’Brien, L, Buitelaar, P.: An analysis of topic modeling for legislative texts. In: Proceedings of the Second Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2017), London, UK (2017)
Kolini, F., Janczewski, L.: Clustering and topic modeling: a new approach for analysis of national cyber security strategies. In: Twenty First Pacific Asia Conference on Information Systems, Langkawi (2017)
Sun, S., Luo, C., Chen, J.: A review of natural language processing techniques for opinion mining systems. Inf. Fusion 36, 10–15 (2017). Elsevier
Phand, S.A., Chakkarwar, V.A.: Enhanced sentiment classification using geo location tweets. In: ICICCT 2018, pp 881–886, IISC Banglore, India (2018)
Chen, H., Xie, L., Leung, C.-C., Lu, X., Ma, B., Li, H.: Modeling latent topics and temporal distance for story segmentation of broadcast news. IEEE/ACM Trans. Audio, Speech, Lang. Process. 25(1), 112–123(2017)
Uys, J.W., du Preez, N.D., Uys, E.W.: Leveraging unstructured information using topic modeling. In: PICMET 2008 Proceedings, pp. 955–961, 27–31 July, Cape Town, South Africa (c) (2008)
Tamane, S.C.: Text analytics for big data. Int. J. Mod. Trends Eng. Res. 02(03) (2015). ISSN: 2349-9745, p-ISSN: 2393-8161
ElShal, S., Mathad, M., Simm, J., Davis, J., Moreau, Y.: Topic modeling of biomedical text. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China (2016). https://doi.org/10.1109/bibm.2016.7822606
Gao, Y., Xu, Y., Li, Y.: Pattern based topics for document modeling in information filtering. IEEE Trans. Knowl. Data Eng. 27(6) (2015)
Ko, N., Jeong, B., Choi, S., Yoon, J.: Identifying product opportunities using social media mining: application of topic modeling and chance discovery theory, pp. 2169–3536 © 2017 IEEE (2017). https://doi.org/10.1109/access
Chien, J.-T.: Hierarchical theme and topic modeling. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 565–578, (2016)
Bulut, A.: TopicMachine: conversion prediction in search advertising using latent topic models. IEEE Trans. Knowl. Data Eng. 26(11) (2014)
Acknowledgements
We like to acknowledge the Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India for supporting Research Facilities. We also like to Thank Head, Computer Science and Engineering Department and Principal, Govt. College of Engineering, Aurangabad, Maharashtra, India for their valuable guidance in different aspect of this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chakkarwar, V., Tamane, S.C. (2020). Quick Insight of Research Literature Using Topic Modeling. In: Zhang, YD., Mandal, J., So-In, C., Thakur, N. (eds) Smart Trends in Computing and Communications. Smart Innovation, Systems and Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-0077-0_20
Download citation
DOI: https://doi.org/10.1007/978-981-15-0077-0_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0076-3
Online ISBN: 978-981-15-0077-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)