Multilingual Sentiment Analysis for Mobile Gaming: A Comparative Study of Machine Learning and Hybrid Deep Learning Approaches

The rapid expansion of the mobile gaming industry has underscored the need to understand user sentiment, with social media platforms like X (Twitter) providing key insights. This study applied sentiment analysis to English and Turkish tweets, utilizing the TEMSAP-CNNLSTM model a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for precise classification of complex textual data. The model’s performance was benchmarked against traditional machine learning methods, including Logistic Regression, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Results revealed that TEMSAP-CNNLSTM consistently achieved superior performance, with the highest Accuracy, Precision, Recall, and F1-Score across both datasets. The model attained 96% accuracy on English and Turkish training datasets and 93% and 92% on English and Turkish test datasets, respectively. These findings highlighted the model’s capability in handling sentiment data, surpassing traditional approaches while demonstrating robust generalizability across languages. The TEMSAP-CNNLSTM model offers valuable insights for mobile game developers and suggests broader applicability for other industries requiring accurate sentiment analysis in multilingual contexts.