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Business intelligence using deep learning techniques for social media contents

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Abstract

Satisfaction Detection is one of the most common issues that impact the business world. So, this study aims to suggest an application that detects the Satisfaction tone that leads to customer happiness for Big Data that came out from online businesses on social media, in particular, Facebook and Twitter, by using two famous methods, machine learning and deep learning (DL) techniques.There is a lack of datasets that are involved with this topic. Therefore, we have collected the dataset from social media. We have simplified the concept of Big Data analytics for business on social media using three of the most famous Natural Language Processing tools, stemming, normalization, and stop word removal. To evaluate the performance of the classifiers, we calculated F1-measure, Recall, and Precision measures. The result showed superiority for the Random Forest classifier the highest value of F1-measure with (99.1%). The best result achieved without applying pre-processing techniques, through Support Vector Machine with F1-measure (93.4%). On the other hand, we apply DL techniques, and we apply the feature extraction method, which includes Word Embedding and Bag of Words on the dataset. The results showed superiority for the Deep Neural Networks DNN algorithm.

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Data availability

The data set used in the work will be available upon request

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Funding

This work was supported by the Hashemite University and AL Zaytoonah University of Jordan.

Author information

Authors and Affiliations

  1. Department of Computer Science, Al-Zaytoonah University of Jordan, Amman, Jordan

    Tarek Kanan, Mohammed Elbes & Odai Sadaqa

  2. Department of Information Technology, Faculty of Prince Al-Hussien Bin Abdullah for IT, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan

    Ala Mughaid

  3. Management Information System Department-Applied Science University, Al Eker, Bahrain

    Riyad Al-Shalabi

  4. Department of Computer Science, Jordan University of Science and Technology, Irbid, Jordan

    Mahmoud Al-Ayyoub

Authors
  1. Tarek Kanan
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  2. Ala Mughaid
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  3. Riyad Al-Shalabi
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  4. Mahmoud Al-Ayyoub
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  5. Mohammed Elbes
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  6. Odai Sadaqa
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Correspondence to Tarek Kanan.

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Kanan, T., Mughaid, A., Al-Shalabi, R. et al. Business intelligence using deep learning techniques for social media contents. Cluster Comput 26, 1285–1296 (2023). https://doi.org/10.1007/s10586-022-03626-y

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