Echoes of Emotion: Enhancing Sentiment Analysis with CNN-GRU Synergy

To conclude, the present research paper has investigated a hybrid Convolutional Neural Network and Gated Recurrent Unit model for sentiment analysis in tweets and Facebook posts. The CNN-GRU hybrid model combines the local feature-capturing capability of CNNs and the sequential dependency management of GRUs to create a holistic sentiment classification model. The following methodology has been used for this study: Data Collection and Preprocessing; Model Design and Architecture; Training and Evaluation and Performance Analysis and Comparison. The obtained results confirm that the CNN-GRU hybrid model outperforms both simple CNN and GRU models as well as the selected machine learning techniques. The accuracy of the model is 89.5%, precision − 89.0%, recall − 89.2%, and F1-score 89.1%. Moreover, the model has been validated using cross-validation and provides a consistent accuracy level on several folds. The confusion matrices and ROC curves have been used to evaluate and visualize the model classification accuracy. The study findings demonstrate that the CNN-GRU is highly efficient at social media sentiment analysis, opening multiple directions for application in market research, social listening, and public opinion monitoring. Further research may optimize the hybrid model for high-performance other user-generated content.