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Business demands for processing unstructured textual data – text mining techniques for companies to implement

Author

Listed:
  • Denitsa ZHECHEVA

    (Konstantin Preslavsky University of Shumen, Shumen, Bulgaria)

  • Nayden NENKOV

    (Konstantin Preslavsky University of Shumen, Shumen, Bulgaria)

Abstract

The rapid development of technology has caused a pervasive change in the way people and businesses live. Making sound business decisions is unthinkable without processing a large amount of data (publicly available and collected on the basis of problems) with high accuracy and quality. The importance of unstructured data acquires various sources is growing. Of particular value is the continuous flow of textual information that is generated every minute around the world in a different form (unstructured textual data). This is also the subject of this article. The aim of the article is to provide an analytical overview of the main methods of word processing that are applicable for pragmatic analysis of information flows from companies, such as: extraction, summarization, grouping and categorization of text. Some methodologies are based on NLP (Natural Language Processing), others on Bayesian logic and statistical theory and practice. From the review of various publications on the topic, conclusions are proposed for their practical applicability. This allows for an objective choice of appropriate tools for processing unstructured information and business intelligence. The results of the study can be successfully used to improve managerial decision-making, improve the quality of work of employees and reduce errors in overall marketing planning.

Suggested Citation

  • Denitsa ZHECHEVA & Nayden NENKOV, 2022. "Business demands for processing unstructured textual data – text mining techniques for companies to implement," Access Journal, Access Press Publishing House, vol. 3(2), pages 107-120, April.
    • Handle: RePEc:aip:access:v:3:y:2022:i:2:p:107-120
    DOI: 10.46656/access.2022.3.2(2)
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    References listed on IDEAS

    as
    1. Gibbert, Michael & Leibold, Marius & Probst, Gilbert, 2002. "Five Styles of Customer Knowledge Management, and How Smart Companies Use Them To Create Value," European Management Journal, Elsevier, vol. 20(5), pages 459-469, October.
    2. Maurice Roux, 2018. "A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 345-366, July.
    3. Yuanzhu Zhan & Kim Hua Tan & Baofeng Huo, 2019. "Bridging customer knowledge to innovative product development: a data mining approach," International Journal of Production Research, Taylor & Francis Journals, vol. 57(20), pages 6335-6350, October.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    clustering; unstructured textual data; business intelligence; NLP (Natural Language Processing); text mining; text extraction; text summarization; text categorization; text retrieval;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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