aspg-18-3050-1791423600 20261008014000 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2024 13 2 Noor alhuda A. Salih Presidency of Thi-Qar University, Thi-Qar, Iraq Osama A. Qasim Department of Computer Engineering Technology, Northern Technical University, 41000, Mosul, Iraq Mohammed S. Noori Department of Computer Engineering Technology, Northern Technical University, 41000, Mosul, Iraq Rabei Raad Ali Department of Computer Engineering Technology, Northern Technical University, 41000, Mosul, Iraq Khawla Ahmad Wali Al Turath University. English Department, Baghdad, Iraq 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. 2024 293 302 3050 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.130223 https://www.americaspg.com/journal/18/article/3050 https://www.americaspg.com/storage/41721122654.pdf