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Cluster Computing
Erschienen in: Cluster Computing 2/2023

03.08.2022

Business intelligence using deep learning techniques for social media contents

verfasst von: Tarek Kanan, Ala Mughaid, Riyad Al-Shalabi, Mahmoud Al-Ayyoub, Mohammed Elbes, Odai Sadaqa

Erschienen in: Cluster Computing | Ausgabe 2/2023

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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.
Vorheriger Artikel The effect of QoS and QoE requirements for designing task processing controller based on fuzzy logic on IoT environments
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Metadaten
Titel
Business intelligence using deep learning techniques for social media contents
verfasst von
Tarek Kanan
Ala Mughaid
Riyad Al-Shalabi
Mahmoud Al-Ayyoub
Mohammed Elbes
Odai Sadaqa
Publikationsdatum
03.08.2022
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 2/2023
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-022-03626-y

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