Comparison of the Performance of a Three-Layer LSTM Model with Classical Algorithms in Sentiment Analysis of Persian Texts in the Automotive Industry - Shojaei Barjouei , Lida; Bakhshandeh, Ghasem; Daniali Deh Douz, Mahmood (Management Strategies and Engineering Sciences, 2027)
In the field of business, sentiment analysis is considered an effective tool for monitoring customer feedback, improving decision-making processes, and enhancing product quality. This study aimed to evaluate the efficiency of different sentiment analysis methods by comparing the performance of a deep learning model based on a three-layer Long Short-Term Memory (LSTM) recurrent neural network with four classical machine learning algorithms, including Linear Support Vector Machine (Linear SVM), Naive Bayes, Multilayer Perceptron (MLP), and Decision Tree. The research data consisted of real user reviews regarding domestic automobiles, which were utilized for model training and evaluation after standard preprocessing procedures. The findings indicated that among the classical algorithms, the Linear SVM model achieved the best performance with an accuracy of 0.90, followed by the MLP model with an accuracy of 0.88, whereas the Naive Bayes and Decision Tree models demonstrated the weakest performance. Compared with these methods, the three-layer LSTM model significantly outperformed the other approaches by achieving an accuracy of 95.4%. This superiority can be attributed to the capability of the LSTM architecture to learn temporal dependencies, capture the sequential structure of sentences, and extract deeper semantic relationships.