CNN-LSTM Architecture for Multi-Task Sentiment and Emotion Classification on Large-Scale Indonesian TikTok Application Reviews

Sentiment and emotion analysis of mobile application reviews has attracted significant attention as a means to understand users’ perceptions and experiences. The research proposes a novel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model for multi-task sentiment and emotion classification on Indonesian TikTok application reviews. A large-scale corpus consisting of 500,000 reviews is collected from the Google Play Store and preprocessed through cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labels (positive, negative, and neutral) are assigned using a lexicon-based approach, while emotion labels are annotated through emoji analysis and word matching based on five basic emotions: anger, fear, happiness, love, and sadness. The proposed CNN-LSTM model is evaluated against a hybrid Bidirectional Encoder Representations from Transformers – Convolutional Neural Network (BERT-CNN) architecture. Experimental results show that the CNN-LSTM model outperforms the BERT-CNN model, achieving an accuracy of 91.30% for sentiment classification and 99.15% for emotion classification, compared to 42.43% and 72.85%, respectively, obtained by the BERT-CNN model. These findings indicate that the CNN-LSTM architecture is more effective in capturing sequential patterns and contextual features in Indonesian review texts, particularly in a multi-task learning setting. Despite its strong performance, the research is limited by its focus on a single platform and the use of lexicon-based automatic labeling, suggesting future work on cross-domain evaluation and manual annotation refinement.