Abstract
Advances in neural network models and deep learning mark great impact on sentiment analysis, where models based on recursive or convolutional neural networks show state-of-the-art results leaving behind non-neural models like SVM or traditional lexicon-based approaches. We present Tree-Structured Gated Recurrent Unit network, which exhibits greater simplicity in comparison to the current state of the art in sentiment analysis, Tree-Structured LSTM model.
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This research was supported in part by PL-Grid Infrastructure. The research was also partially financed by AGH University of Science and Technology Statutory Fund.
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Kuta, M., Morawiec, M., Kitowski, J. (2017). Sentiment Analysis with Tree-Structured Gated Recurrent Units. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_9
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DOI: https://doi.org/10.1007/978-3-319-64206-2_9
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