2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 2 , PP: 293-302, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Noor alhuda A. Salih 1 , Osama A. Qasim 2 , Mohammed S. Noori 3 , Rabei Raad Ali 4 * , Khawla Ahmad Wali 5
Doi: https://doi.org/10.54216/JISIoT.130223
The Machine Learning domain has made a major process with the progression of state-of-the-art technologies. Since current algorithms often don’t provide palatable learning performance, it is necessary to continually upgrade them. This paper has illustrated the comparison of the Long Short-Term Memory (LSTM) model and the Artificial Neural Networks (ANN) model in the prediction of the Internet Movie Database (IMDb) website. These evaluations were then related to sentiment assessment approaches to evaluate their predicted accuracy and performances. The results demonstrate that the ANN model outperforms the LSTM model with a high accuracy rate in terms of the prediction accuracy and loss indicators for the IMDb movie review’s sentiment analysis task in terms of the prediction accuracy and loss indicators for the IMDb movie review’s sentiment analysis task. The accuracy of prediction on the test dataset of the ANN model is 83.5 % and the LSTM model is 83.5%. Therefore, it can be concluded that the standard artificial neural network model that was utilized is an appropriate technique for sentiment assessment tasks in IMDb rating text data.
Long Short-Term Memory (LSTM) , Artificial Neural Networks (ANN) , Internet Movie Database (IMDb) , Prediction Accuracy
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