Social Media Sentiment Analysis Classification Leveraging Hybrid Deep Learning Methods

Abstract

Social media platforms are one of the most popular platforms for users to express their opinions and sentiments concerning various products, services, and organizations. This study presents the models that can successfully analyze sentiment through text on social media platforms using hybrid deep learning methods. The proposed models were applied to the "U.S. Airline Dataset" obtained from the Kaggle platform. After the text cleaning phase, the BERT embeddings were implemented on the dataset. The resulting preprocessed data set was used in the training of three deep learning-based hybrid algorithms, namely, CNN-GRU, BiLSTM-GRU, and RNN-GRU. The experimental results revealed that the best result was achieved by the CNN-GRU model, with an accuracy of 0.79 and an F-Score of 0.79.