Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model
In: Electronics, Vol 12, Iss 1302, p 1302 (2023, Jg. 12 (2023), Heft 1302, p 1302
academicJournal
Zugriff:
Widespread fear and panic has emerged about COVID-19 on social media platforms which are often supported by falsified and altered content. This mass hysteria creates public anxiety due to misinformation, misunderstandings, and ignorance of the impact of COVID-19. To assist health professionals in addressing this epidemic more appropriately at the onset, sentiment analysis can potentially help the authorities for devising appropriate strategies. This study analyzes tweets related to COVID-19 using a machine learning approach and offers a high-accuracy solution. Experiments are performed involving different machine and deep learning models along with various features such as Word2vec, term-frequency, term-frequency document frequency, and feature fusion of both feature-generating approaches. The proposed approach combines the extra tree classifier and convolutional neural network and uses feature fusion to achieve the highest accuracy score of 99%. The proposed approach obtains far better results than existing sentiment analysis approaches.
Titel: |
Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model
|
---|---|
Autor/in / Beteiligte Person: | Hamza Ahmad Madni ; Umer, Muhammad ; Abuzinadah, Nihal ; Hu, Yu-Chen ; Saidani, Oumaima ; Alsubai, Shtwai ; Hamdi, Monia ; Ashraf, Imran |
Link: | |
Zeitschrift: | Electronics, Vol 12, Iss 1302, p 1302 (2023, Jg. 12 (2023), Heft 1302, p 1302 |
Veröffentlichung: | MDPI AG, 2023 |
Medientyp: | academicJournal |
ISSN: | 2079-9292 (print) |
DOI: | 10.3390/electronics12061302 |
Schlagwort: |
|
Sonstiges: |
|