Vol.9, No.4, November 2020.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Sentiment Analysis towards Actionable Intelligence via Deep Learning


Shorouq Fathi Eletter


© 2020 Shorouq Fathi Eletter, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 9, Issue 4, Pages 1663-1668, ISSN 2217-8309, DOI: 10.18421/TEM94-44, November 2020.


Received: 29 July 2020.

Revised:   24 September 2020.
Accepted:  06 October 2020.
Published: 27 November 2020.




The exponential growth of unstructured data and the ability of businesses to utilize such data in decision-making have led to competitive advantages. The knowledge provided by analyzing unstructured data is crucial for product developers or service providers because it might affect the sustainability of the business. Sentiment analysis is used to gain an understanding of the attitudes, opinions, and emotions expressed within an online review. Naïve Bayes (NB), logistic regression (LR), decision trees (DT), deep learning (DL), and support vector machines (SVM) were used to build a classification model. In the data mining settings, the classification accuracy is the best metric to highlight the best classifier. The DL classifier outperformed other models in terms of accuracy rate. Classifying customers' feelings toward a product or service is critical for providing actionable insights. Utilizing such models will help to analyze huge volumes of reviews, saving both time and costs.


Keywords –Sentiment analysis, Text mining, Deep learning, Computational cost, Classification, Natural language processing (NLP).



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