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Understanding Customers' Opinion using Web Scraping and Natural Language Processing

Author

Listed:
  • Alin-Gabriel Vaduva

    (The Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, Romania)

  • Simona-Vasilica Oprea

    (The Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, Romania)

  • Dragos-Catalin Barbu

    (The Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, Romania)

Abstract

The web offers large volumes of data that is unstructured and fails to be further processed if not extracted and organized into local variables or into databases. In this paper, we aim to extract data from the Internet using web scraping and analyse it with Natural Language Processing (NLP). Our purpose is to understand customers’ opinions by extracting reviews and investigating them in Python. The positive or negative insight of the reviews, along with the word cloud offer additional tools to understand the customers, predict their behaviour and underpin problems signalled in the reviews. TextBlob and BERTweet are applied to analyse the reviews. To enhance the comprehension of the outcomes, a comparison is drawn between the classifications generated by the BERTweet model and those provided by the TextBlob API, a widely used Python library for performing various NLP tasks. Furthermore, the reviews are pre-processed to clean them from line breaks, punctuation characters etc. and a n-grams analysis is performed to better understand the positive and negative reviews. The frequency of the reviews displays the concrete problems faced by customers visiting the hotel in various seasons. It helps decision makers to take measures and improve the quality of the hotel services.

Suggested Citation

  • Alin-Gabriel Vaduva & Simona-Vasilica Oprea & Dragos-Catalin Barbu, 2023. "Understanding Customers' Opinion using Web Scraping and Natural Language Processing," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 537-544, August.
    • Handle: RePEc:ovi:oviste:v:xxiii:y:2023:i:1:p:537-544
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    References listed on IDEAS

    as
    1. Yue Kang & Zhao Cai & Chee-Wee Tan & Qian Huang & Hefu Liu, 2020. "Natural language processing (NLP) in management research: A literature review," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 139-172, April.
    2. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
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    More about this item

    Keywords

    web scraping; booking; customers opinions; natural language processing;
    All these keywords.

    JEL classification:

    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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