Abstract
Topic modeling is a text mining technique that revolves around extracting latent topics from a collection of documents. Although the majority of research within the field of topic modeling has been conducted in the English language. Nonetheless, in recent years, there has been an interest in employing the topic modeling methodology within the Arabic language, although its utilization remains somewhat restricted in this language. In this paper, we propose a comparison among various techniques commonly utilized in topic modeling. These techniques include a Probabilistic model, specifically Latent Dirichlet Allocation (LDA), as well as matrix factorization methods like Non-Negative Matrix Factorization (NMF) and Latent Semantic Indexing (LSI). Additionally, we incorporate a transformer-based model known as BERTopic. The implementation was applied to the Arabic language, and the algorithms were trained using the TF-IDF text representation. This choice aimed to ensure a fair comparison between the algorithms. The evaluation of each model is conducted using topic coherence as the metric. The results indicate that both NMF and Bertopic give an excellent performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Hu, Y., Boyd-Graber, J., Satinoff, B., Smith, A.: Interactive topic modeling. Mach. Learn. 95(3), 423–469 (2014). https://doi.org/10.1007/s10994-013-5413-0
Crain, S.P., Zhou, K., Yang, S.H., Zha, H.: Dimensionality reduction and topic modeling: from latent semantic indexing to latent dirichlet allocation and beyond. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 129–161. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_5
Abdelrazek, A., Eid, Y., Gawish, E., Medhat, W., Hassan, A.: Topic modeling algorithms and applications: a survey. Inf. Syst. 112, 102131 (2023). https://www.sciencedirect.com/science/article/pii/S0306437922001090
Atagün, E., Hartoka, B., Albayrak, A.: Topic modeling using lda and bert techniques: Teknofest example. In: 2021 6th International Conference on Computer Science and Engineering (UBMK), pp. 660–664 (2021)
George, L., Sumathy, P.: An integrated clustering and BERT framework for improved topic modeling. Int. J. Inf. Technol. 15(4), 2187–2195 (2023). https://doi.org/10.1007/s41870-023-01268-w
Abuzayed, A., Al-Khalifa, H.: Bert for Arabic topic modeling: an experimental study on bertopic technique. Procedia Comput. Sci. 189, 191–194 (2021). https://www.sciencedirect.com/science/article/pii/S1877050921012199
Al Qudah, I., Hashem, I., Soufyane, A., Chen, W., Merabtene, T.: Applying latent dirichlet allocation technique to classify topics on sustainability using Arabic text. In: Arai, K. (eds.) Intelligent Computing, vol. 506, pp. 630–638. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10461-9_43
Alhaj, F., Al-Haj, A., Sharieh, A., Jabri, R.: Improving arabic cognitive distortion classification in twitter using bertopic. Int. J. Adv. Comput. Sci. Appl. 13(1) (2022). https://doi.org/10.14569/IJACSA.2022.0130199
Almuzaini, H.A., Azmi, A.M.: An unsupervised annotation of Arabic texts using multi-label topic modeling and genetic algorithm. Expert Syst. Appl. 203, 117384 (2022). https://www.sciencedirect.com/science/article/pii/S0957417422007266
Alhawarat, M., Hegazi, M.: Revisiting k-means and topic modeling, a comparison study to cluster arabic documents. IEEE Access 6, 42740–42749 (2018)
Nouar, F., Belhadef, H.: A deep neural network model with multihop self-attention mechanism for topic segmentation of texts. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds.) IRICT 2020. LNDECT, vol. 72, pp. 407–417. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70713-2_38
Yang, Y.: Research and realization of internet public opinion analysis based on improved tf - idf algorithm. In: 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), pp. 80–83 (2017)
Liang, M., Niu, T.: Research on text classification techniques based on improved TF-IDF algorithm and LSTM inputs. Procedia Comput. Sci. 208, 460–470 (2022). 7th International Conference on Intelligent, Interactive Systems and Applications
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(2), 993–1022 (2003)
Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of LREC 2010 Workshop New Challenges for NLP Frameworks, pp. 46–50 (2010)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1990)
Pauca, V.P., Piper, J., Plemmons, R.J.: Nonnegative matrix factorization for spectral data analysis. Linear Algebra Appl. 416(1), 29–47 (2006). https://www.sciencedirect.com/science/article/pii/S002437950500340X
Grootendorst, M.: Bertopic: neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794 (2022)
Chen, W., Rabhi, F., Liao, W., Al-Qudah, I.: Leveraging state-of-the-art topic modeling for news impact analysis on financial markets: a comparative study. Electronics 12(12) (2023). https://www.mdpi.com/2079-9292/12/12/2605
Einea, O., Elnagar, A., Al Debsi, R.: Sanad: single-label Arabic news articles dataset for automatic text categorization. Data Brief 25, 104076 (2019)
Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. Proc. GSCL 30, 31–40 (2009)
Michael, R., Andreas, B., Alexander, H.: Exploring the space of topic coherence measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 399–408 (2015). https://doi.org/10.1145/2684822.2685324
Syed, S., Spruit, M.: Full-text or abstract? Examining topic coherence scores using latent dirichlet allocation. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 165–174 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Djemmal, I., Belhadef, H. (2024). Comparative Analysis of Topic Modeling Algorithms Based on Arabic News Documents. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-59707-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-59707-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-59706-0
Online ISBN: 978-3-031-59707-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)