Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3050 2019 2019 Presidency of Thi-Qar University, Thi-Qar, Iraq Rabei Rabei Department of Computer Engineering Technology, Northern Technical University, 41000, Mosul, 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 Al Turath University. English Department, Baghdad, Iraq Khawla Ahmad Wali 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 2024 293 302 10.54216/JISIoT.130223 https://www.americaspg.com/articleinfo/18/show/3050