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.

Keywords - Machine learning, Sentiment analysis, Twitter, Text mining

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