Comparing the Performance of Deep Learning Architectures for Sentiment Analysis
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Keywords:
Deep learning, LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), 1D CNN (Convolutional Neural Network), Emotion ClassificationAbstract
With the advancement of technology, people frequently express their feelings and thoughts in
environments such as social media. Natural language processing applications are also very much on the
agenda. Thanks to sentiment analysis, inferences can be made by analyzing them. In this study, different
methods of emotion classification with deep learning were investigated and applied. IMDb dataset created
from movie reviews was used as a dataset. In sentiment classification, four different architectures were
applied to the same dataset and compared. As a result of this comparison, it was observed that the 1D CNN
model gave the best results. It was concluded that this architecture is efficient and fast for such studies.
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