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Academic Journal of Computing & Information Science, 2023, 6(1); doi: 10.25236/AJCIS.2023.060111.

Research on Topic Discovery and Evolution Trend Based on Temporal Keyword Characteristics Analysis


Wan Wang

Corresponding Author:
Wan Wang

College of Information Engineering, Nanjing University of Finance & Economics, Nanjing, China


In order to mine the research topics in massive articles, sort out the evolution context and correlation relationship of research topics, enhance the scientificity and vividness of evolution results, this paper proposes the concept of temporal influence factor as an important feature in keyword extraction. Using the time window method, the topic model is used to mine and identify topics and perform visual analysis. It is verified that keyword extraction with time series features can improve the effect of topic model. Through visualization, we can not only observe the overall trend of topic popularity, but also analyze the evolution of topic content in each time period and observe the trend of splitting and merging.


Time Series Keyword, Topic Evolution, Keyword Extraction, Visual Analysis

Cite This Paper

Wan Wang. Research on Topic Discovery and Evolution Trend Based on Temporal Keyword Characteristics Analysis. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 1: 70-76. https://doi.org/10.25236/AJCIS.2023.060111.


[1] Wang K, Gao JP, Pan YT, Chen Y. (2021) Research on Multi-position Research Topic Recognition and Evolutionary Path Method. Library and Information Work. 65(11), 113-122.

[2] Chen, B., Tsutsui, S., Ding, Y., Ma, F. (2017) Understanding the topic evolution in a scientific domain: an exploratory study for the field of information retrieval. Journal of Informetrics. 11(4), 1175-1189.

[3] FRANK E, PAYNTER G W, WITTEN I H. (1999) Domain-specific keyphrase extraction. International Joint Conference on Artificial Intelligence. 2, 668-673. 

[4] Turney, P. D. (2000) Learning algorithms for keyphrase extraction. Information Retrieval. 2(4), 303-336.

[5] Gollapalli, S. D., Li, X., Peng, Y. (2017) Incorporating expert knowledge into keyphrase extraction. National Conference on Artificial Intelligence. 3180-3187.

[6] Papagiannopoulou, E. T. G. (2020) A Review of Keyphrase Extraction. Wiley interdisciplinary reviews. Data mining and knowledge discovery, 10(2).

[7] Jones, K. S. (2004) A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 60(5), 493-502.

[8] Liu, Z., Peng, L., Zheng, Y., Sun, M.. (2009) Clustering to find exemplar terms for keyphrase extraction. Conference on Empirical Methods in Natural Language Processing. 257-266. 

[9] Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A.. (2018) A text feature based automatic keyword extraction method for single documents. European conference on information retrieval. 63, 684-691.

[10] Mihalcea, R., Tarau, P.. (2004) Textrank: bringing order into texts. Proceedings of 2004 Conference on Empirical Methods in Natural Language Processing. Barcelona, Spain. 404-411.

[11] Danesh, S., Sumner, T., Martin, J. H.. (2015) SGRank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction. Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics. 117-126.

[12] Shan B, Li F. (2010) Overview of research methods of topic evolution based on LDA. Journal of Chinese Information Processing. 24(6), 43-50.

[13] Wang, X., Mccallum, A.. (2006) Topics over time: a non-Markov continuous-time model of topical trends. Acm Sigkdd International Conference on Knowledge Discovery & Data Mining. ACM.

[14] Griffiths, T. L., Steyvers, M.. (2004) Finding Scientific Topics. Proceedings of the National Academy of Sciences of the United States of America. 101(S1), 5228-5235.

[15] Blei, D. M., Lafferty, J. D.. (2006) Dynamic topic models. Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006.

[16] Kui L, Xu HY, Hu ZY. (2016) Multi-pattern recognition and prediction of subject evolution path. Library And Information Service. 60(13), 71-81.

[17] Gao N, Peng DY, Fu JY. (2020) Analysis of Advanced Technological Evolution Based on Patent IPC Classification and Text Information--A Case Study in the Field of Artificial Intelligence. Information studies: Theory & Application. 43(4), 123-129.

[18] Hou JH, Li LJ, Yang XC. (2018) Frontier Prediction of Large Data Research Based on the Transformation of Citation Network Structure. Information Science. 36(6), 142-148.