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Unsupervised consumer intention and sentiment mining from microblogging data as a business intelligence tool

Author

Listed:
  • Symeon Symeonidis

    (Democritus University of Thrace)

  • Georgios Peikos

    (University of Milano-Bicocca)

  • Avi Arampatzis

    (Democritus University of Thrace)

Abstract

The present study aims to create a framework that analyses user posts related to a product of interest on social networking platforms. More precisely, by applying information mining techniques, posts are categorised according to the intention they express, the sentiment polarisation, and the type of opinion. The model operates based on linguistic rules, machine learning, and combinations. Six different methodologies are implemented to extract intent, sentiment, and type of opinion from a tweet. The final model automatically detects intention to buy or not to buy the product, intention to compare the product with other competitors, and finally, intention to search for information about the product. It then categorises the text according to the sentiment and depending on their expressed opinion. The dataset comprises tweets for each day of the iPhone 5’s life cycle, corresponding to 365 days. Additionally, it demonstrated that the business’s external or internal decisions affect the public purchasing audience’s opinions, sentiments, and intentions expressed on social media. Lastly, as a Business Intelligence tool, the framework recognises and analyses these points, which contribute substantially to the company’s decision-making through the findings.

Suggested Citation

  • Symeon Symeonidis & Georgios Peikos & Avi Arampatzis, 2022. "Unsupervised consumer intention and sentiment mining from microblogging data as a business intelligence tool," Operational Research, Springer, vol. 22(5), pages 6007-6036, November.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00714-0
    DOI: 10.1007/s12351-022-00714-0
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    References listed on IDEAS

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