Vol.13, No.2, May 2024.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Deep Sentiment Analysis System with Attention Mechanism for the COVID-19 Vaccine


Mustafa S. Khalefa, Zainab Amin Al-Sulami, Eman Thabet Khalid, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Mustafa A. Al Sibahee, Junchao Ma, Iman Qays Abduljaleel


© 2024 Zaid Ameen Abduljabbar, 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 13, Issue 2, Pages 1470-1480, ISSN 2217-8309, DOI: 10.18421/TEM132-61, May 2024.


Received: 14 November 2023.

Revised:   29 February 2024.
Accepted: 07 March 2024.
Published: 28 May 2024.




Sentiment analysis has attracted huge interest, which has been a trend topic in last years. It has significant applications in several areas, such as marketing based on opinion recognition and mining, movie reviews, product reviews, and healthcare-based sentiment understanding. In this paper, COVID-19 vaccine has been considered as an experimental design and performs sentiment analysis to understand the opinions of the public toward getting vaccinated. The topic of vaccination has been associated with a great deal of hesitancy and different points of view from people who may trust or distrust taking the vaccine. The proposed system aims to understanding data from chats related to the COVID-19 vaccine on the Twitter platform. A deep learning framework has been built based on a bidirectional long-short-term memory (Bi-LSTM) network and use an attention mechanism to obtain precise results. Three categories are used to classify the obtained results as positive, negative, neutral. The overall accuracy of the proposed method is found to be 94%, in addition accuracy of our case study results show for the three opinion mining classes of negative, neutral, and positive on the training set was 0.96%, 0.89%, and 0.95%, respectively. On the test data, the accuracy was 0.96% for negative sentiment, 0.88% for neutral sentiment, and 0.95% for positive sentiment.


Keywords – COVID-19, COVID-19 vaccine, SARS-COV-2 vaccine, Bidirectional LSTM, Attention mechanism.



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