Hybrid Technique of Topic Modelling and Text Summarization: A Case Study on Predicting Trends in Green Computing

doi:10.23940/ijpe.24.03.p2.139148

Abstract

Abstract: Text mining techniques are used for trend prediction using keyword analysis; however, these processes result in the formation of relevant and irrelevant keywords. Based on the keywords, various clusters are formed, resulting in various topics. Due to the presence of irrelevant keywords, there are chances of the formation of wrong topics. To overcome this problem, this research contributes to developing an algorithm that deals with topic prediction using a noble technique wherein text summarization is inculcated into topic modeling algorithms. This research focuses on implementing text summarizing to generate summaries of published publications by diverse researchers using the Genism library with an extractive text summarization approach and then applying text mining to it to predict trends in various fields. The current approach was compared with existing techniques based on the parameters used in automatic and semi-automatic text mining techniques.

Key words: natural language processing (NLP), text summarization, latent semantic analysis (LSA), latent dirichlet allocation (LDA), extractive text summarization (ETS), abstractive text summarization (ATS)

Mansi Pandey, Chetan Sharma, Shamneesh Sharma, and Trapty Aggarwal. Hybrid Technique of Topic Modelling and Text Summarization: A Case Study on Predicting Trends in Green Computing [J]. Int J Performability Eng, 2024, 20(3): 139-148.

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References

[1] Gul S., Bano S., andShah T.Exploring Data Mining: Facets and Emerging Trends. Digital Library Perspectives, vol. 37, no. 4, pp. 429-448, 2021.
[2] Rahman, U. and Mahbub, M.U.Application of Classification Models on Maintenance Records through Text Mining Approach in Industrial Environment. Journal of Quality in Maintenance Engineering, vol. 29, no. 1, pp. 203-219, 2023.
[3] Kumar Y., Kaur K., andKaur S.Study of Automatic Text Summarization Approaches in Different Languages. Artificial Intelligence Review, vol. 54, no. 8, pp. 5897-5929, 2021.
[4] Kumar, S. and Solanki, A.An Abstractive Text Summarization Technique using Transformer Model with Self-Attention Mechanism.Neural Computing and Applications, pp. 1-20, 2023.
[5] Navjord, J.J. and Korsvik, J.M.R.Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers (Master's thesis, Norwegian University of Life Sciences, Ås), 2023.
[6] Salloum S.A., Khan R., andShaalan K.A Survey of Semantic Analysis Approaches. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), Springer International Publishing, pp. 61-70, 2020.
[7] Lauriola I., Lavelli A., andAiolli F.An Introduction to Deep Learning in Natural Language Processing: Models, Techniques, and Tools. Neurocomputing, vol. 470, pp. 443-456, 2022.
[8] Murshed B.A.H., Mallappa, S., Abawajy, J., Saif, M.A.N., Al-Ariki, H.D.E., and Abdulwahab, H.M. Short Text Topic Modelling Approaches in the Context of Big Data: Taxonomy, Survey, and Analysis. Artificial Intelligence Review, vol. 56, no. 6, pp. 5133-5260, 2023.
[9] Burkhardt, S. and Kramer, S.Online Multi-Label Dependency Topic Models for Text Classification. Machine Learning, vol. 107, pp. 859-886, 2018.
[10] Dou W., Yu L., Wang X., Ma Z., andRibarsky W.Hierarchicaltopics: Visually Exploring Large Text Collections using Topic Hierarchies. IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2002-2011, 2013.
[11] Chan S.W.,T'SOU, B.K., and Lai, T.B. An Investigation of Salient Information in Textual Summarization. International Journal of Computer Processing of Oriental Languages, vol. 13, no. 04, pp. 265-290, 2000.
[12] Lin, C.Y. and Hovy, E. from Single to Multi-Document Summarization. InProceedings of the 40th annual meeting of the association for computational linguistics (pp. 457-464), 2002.
[13] Gandomi, A. and Haider, M.Beyond the Hype: Big Data Concepts, Methods, and Analytics. International journal of information management, vol. 35, no. 2, pp. 137-144, 2015.
[14] Shahbazi, Z. and Byun, Y.C.Topic Modeling in Short-Text using Non-Negative Matrix Factorization Based on Deep Reinforcement Learning. Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 753-770, 2020.
[15] Sharma C., Sharma S., andSaksh. Latent DIRICHLET Allocation (LDA) Based Information Modelling on BLOCKCHAIN Technology: A Review of Trends and Research Patterns Used in Integration. Multimedia Tools and Applications, vol. 81, no. 25, pp. 36805-36831, 2022.
[16] Alomari A., Idris N., Sabri A.Q.M., and Alsmadi, I. Deep Reinforcement and Transfer Learning for Abstractive Text Summarization: A Review. Computer Speech & Language, vol. 71, pp. 101276, 2022.
[17] Malik A., Sharma S., Batra I., Sharma C., Kaswan M.S., andGarza-Reyes, J.A. Industrial Revolution and Environmental Sustainability: An Analytical Interpretation of Research Constituents in Industry 4.0. International Journal of Lean Six Sigma, vol. 15, no. 1, pp. 22-49, 2024.
[18] Salehani Y.E., Arabnejad E., Rahiche A., Bakhta A., andCheriet M.MSdB-NMF: MultiSpectral Document Image Binarization Framework via Non-Negative Matrix Factorization Approach. IEEE Transactions on Image Processing, vol. 29, pp. 9099-9112, 2020.
[19] Alqaisi R., Ghanem W., andQaroush A.Extractive Multi-Document Arabic Text Summarization using Evolutionary Multi-Objective Optimization with K-Medoid Clustering. IEEE Access, vol. 8, pp. 228206-228224, 2020.
[20] Widyassari A.P., Rustad S., Shidik G.F., Noersasongko E., Syukur A., andAffandy A.Review of Automatic Text Summarization Techniques & Methods. Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 4, pp. 1029-1046.
[21] Gambhir, M. and Gupta, V.Recent Automatic Text Summarization Techniques: A Survey. Artificial Intelligence Review, vol. 47, pp. 1-66, 2017.
[22] Lamsiyah S.,El Mahdaouy, A., El Alaoui, S.O., and Espinasse, B. A Supervised Method for Extractive Single Document Summarization Based on Sentence Embeddings and Neural Networks. In Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) Volume 4-Advanced Intelligent Systems for Applied Computing Sciences. Springer International Publishing, pp. 75-88, 2020.
[23] Maulud D.H., Zeebaree S.R., Jacksi K., Sadeeq M.A.M., and Sharif, K.H. State of Art for Semantic Analysis of Natural Language Processing. Qubahan academic journal, vol. 1, no. 2, pp. 21-28, 2021.
[24] Verma, P. and Verma, A.A Review on Text Summarization Techniques. Journal of scientific research, vol. 64, no. 1, pp. 251-257, 2020.
[25] Rani, U. and Bidhan, K.Comparative Assessment of Extractive Summarization: Textrank Tf-Idf and Lda. Journal of Scientific Research, vol. 65, no. 1, pp. 304-311, 2021.
[26] Chauhan, U. and Shah, A.Topic Modeling using Latent Dirichlet Allocation: A Survey. ACM Computing Surveys (CSUR), vol. 54, no. 7, pp. 1-35, 2021.
[27] Ghanem F.A., Padma M.C., andAlkhatib R.Automatic Short Text Summarization Techniques in Social Media Platforms. Future Internet, vol. 15, no. 9, pp. 311, 2023.
[28] Srivastava R., Singh P., Rana K.P.S., and Kumar, V. A Topic Modeled Unsupervised Approach to Single Document Extractive Text Summarization. Knowledge-Based Systems, vol. 246, pp. 108636, 2022.
[29] Sharma C., Sakhuja S., andNijjer S.Recent Trends of Green Human Resource Management: Text Mining and Network Analysis. Environmental Science and Pollution Research, vol. 29, no. 56, pp. 84916-84935, 2022.
[30] Karmakar S., Sivakumar N., andPillai A.S.Exploring Satisfaction Level of Customers in Restaurants by using Latent Dirichlet Allocation (LDA) Algorithm. In2023 International Conference on Inventive Computation Technologies (ICICT), IEEE, pp. 880-887, 2023.
[31] Akuma S., Lubem T., andAdom I.T.Comparing Bag of Words and TF-IDF with Different Models for Hate Speech Detection from Live Tweets. International Journal of Information Technology, vol. 14, no. 7, pp. 3629-3635, 2022.
[32] Egger, R. and Yu, J.A Topic Modeling Comparison between Lda, Nmf, Top2vec, and Bertopic to Demystify Twitter Posts. Frontiers in sociology, vol. 7, pp. 886498, 2022.