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Correlation analysis of short text based on network model

Author

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  • Yan, Dongyang
  • Li, Keping
  • Ye, Jingjing

Abstract

Correlation of words in the text is of great importance in text analysis like text retrieval, keywords extraction, and text clustering. For short text, because of the limited information of text content, it is difficult to catch the correlation well among words. In this paper, we propose an algorithm based on the complex network to calculate the correlation of words in short texts. A new variable Edge-degree is proposed and used in studying the network model of texts. By using fluctuation analysis, we give the condition that Edge-degree correlation between words exists beyond nearest neighbors. Further analysis shows that numerical results of the fluctuation function of Edge-degree act a power law distribution and that the scaling exponent diverges at a long distance under the finite size effect and varies in different texts. The fluctuation function separates the words in a text into different clusters, and this property is used to measure inner-correlation of different words. Hub nodes act a significant influence on the long-range Edge-degree correlation through changing the linear trend of the fluctuation function in a log–log plot.

Suggested Citation

  • Yan, Dongyang & Li, Keping & Ye, Jingjing, 2019. "Correlation analysis of short text based on network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119309860
    DOI: 10.1016/j.physa.2019.121728
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    Cited by:

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    2. Gangwei Cai & Baoping Zou & Xiaoting Chi & Xincheng He & Yuang Guo & Wen Jiang & Qian Wu & Yujin Zhang & Yanna Zhou, 2023. "Neighborhood Spatio-Temporal Impacts of SDG 8.9: The Case of Urban and Rural Exhibition-Driven Tourism by Multiple Methods," Land, MDPI, vol. 12(2), pages 1-37, January.
    3. Kyriazopoulos Georgios & Sariannidis Nikolaos & Parpoutzidou Androniki, 2020. "Evaluation of the main African Stock Exchanges Markets for Foreign Direct Investments. A Statistical Approach," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 10(5), pages 1-13.
    4. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.
    5. Leandro Pereira & Rita Carvalho & Álvaro Dias & Renato Costa & Nelson António, 2021. "How Does Sustainability Affect Consumer Choices in the Fashion Industry?," Resources, MDPI, vol. 10(4), pages 1-30, April.
    6. Pol Castellano-Escuder & Raúl González-Domínguez & Francesc Carmona-Pontaque & Cristina Andrés-Lacueva & Alex Sánchez-Pla, 2021. "POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-15, July.

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