Skip to main content

Sentence-Based Topic Modeling Using Lexical Analysis

  • 830 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 814)


Data is not meaningful unless its information could be extracted. In every second in this world, we are generating millions of data over the Internet in different form. Most of them are in text format. Usually, data is written based on any topic, or sometimes on few topics. Following this, identifying topic of any text data is very important. Topic identification may help text summarization tools, text classification tool, etc. Machine learning applications may need less training on their data, only if once the topic of text is identified. Therefore, the demand of topic modeling is higher than ever right now. Data scientists are working day and night to make it more effective and accurate using different methods. Topic modeling focuses on the keywords that can express or identify the topic discussed in the document. Topic modeling can save a lot of time by releasing its user from page-to-page manual reviewing. In this paper, a model has been proposed to find out topic of a document. This model works based on the relations between most frequent words and their relation with sentences in the document. This model can be used to increase the accuracy of the topic modeling.


  • Topic Model
  • Text Summarization
  • Text Categorization Tools
  • Latent Dirichlet Allocation (LDA)
  • Valid Word

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-13-1501-5_42
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-981-13-1501-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 1


  1. Gambhir, M., Gupta, V.: Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 47(1), 1–66 (2017).

    CrossRef  Google Scholar 

  2. Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pp. 487–494 (2004)

    Google Scholar 

  3. Tsai, F.S.: A tag-topic model for blog mining. Expert Syst. Appl. 38(5), 5330–5335 (2011)

    CrossRef  Google Scholar 

  4. Liu, Y., Niculescu-Mizil, A., Gryc, W.: Topic-link LDA: joint models of topic and author community. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 665–672 (2009)

    Google Scholar 

  5. Rakshit, G., Ghosh, A., Bhattacharyya, P., Haffri, G.: Automated analysis of Bangla poetry for classifiation and poet identifiation. IITB-Monash Research Academy, India, IIT Bombay, India Monash University, Australia

    Google Scholar 

  6. Das, A., Bandyopadhyay, S.: Topic-based Bengali opinion summarization

    Google Scholar 

  7. Jiang, H., Zhou, R., Zhang, L., Zhang, Y.: A topic model based on poisson decomposition. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, pp 1489–1498, November (2017)

    Google Scholar 

  8. Ruohonen, J.: Classifying web exploits with topic modeling. In: 28th International Workshop on Database and Expert Systems Applications, Lyon, France (2017).

  9. Karami, A., Gangopadhyay, A., Zhou B., Kharrazi, H.: Fuzzy approach topic modeling for health and medical corpora. Int. J. Fuzzy Syst. (2017)

    Google Scholar 

  10. Zhai, C.: Probabilistic topic models for text data retrieval and analysis. In: 40th International ACM SIGIR Conference, Shinjuku, Tokyo, Japan, pp. 1399–1401 (2017)

    Google Scholar 

Download references


We would like to thank Daffodil International University and DIU NLP and Machine Learning Research LAB for all their support and help.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Shahinur Rahman .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Rahman, S., Abujar, S., Mazharul Hoque Chowdhury, S.M., Saifuzzaman, M., Hossain, S.A. (2019). Sentence-Based Topic Modeling Using Lexical Analysis. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 814. Springer, Singapore.

Download citation