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- W4387476101 abstract "Social media platforms are one of the most popular platforms for users to express their opinionsand sentiments concerning various products, services, and organizations. This study presents the modelsthat can successfully analyze sentiment through text on social media platforms using hybrid deep learningmethods. The proposed models were applied to the U.S. Airline Dataset obtained from the Kaggleplatform. After the text cleaning phase, the BERT embeddings were implemented on the dataset. Theresulting 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 resultwas achieved by the CNN-GRU model, with an accuracy of 0.79 and an F-Score of 0.79." @default.
- W4387476101 created "2023-10-11" @default.
- W4387476101 date "2023-01-01" @default.
- W4387476101 modified "2023-10-11" @default.
- W4387476101 title "Social Media Sentiment Analysis Classification Leveraging Hybrid Deep Learning Methods" @default.
- W4387476101 doi "https://doi.org/10.59287/icias.1615" @default.
- W4387476101 hasPublicationYear "2023" @default.
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