Abstract—Large amounts of data are generated from social media. The need to extract meaningful information from big data, classify it into different categories, and predict user sentiment is crucial. Text classification is a representative research topic in the field of natural language processing that categorizes unstructured text data into sentiments to make it more meaningful. Improving word and text category accuracy requires more precise text classification methods. Deep Learning models developed and implemented in this field have shown progress, but further improvement is still needed. This paper utilizes the NLP process on a WhatsApp group dataset to determine sentiment, testing it with five Deep Learning models: Neural Network, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory, Convolutional Neural Network (CNN), and proposes a hybrid CNN-BiLSTM model. The proposed model employs feature extraction and a hybrid architecture with activations, dropouts, filters, kernel sizes, and different layers to classify text sentiment. To verify the performance of the proposed model, it is compared with previous studies. In single-model testing, the Long Short-Term Memory and BiLSTM achieves the best accuracy of 81%. Meanwhile, the proposed model has reached an accuracy of 88% on the utilized dataset. By comparing the performance of the proposed model with previous studies, the proposed model offers better sentiment classification performance.
 
Keywords—text classification, WhatsApp group, hybrid CNN-BiLSTM


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