Abstract
Increasing volume of customer reviews over the commercial Web sites have created a demand for the construction of automated content analysis systems. However, present techniques mainly focus on traditional bag-of-words (BOW) and statistical language models, ignoring semantic compositions. In contrast, deep neural networks (DNN) have exhibited greater stability in equipping on-scale sentiment prediction. Particularly, deep recursive neural networks (Deep-RNN) have been consistently used for capturing semantic compositions in natural language content when represented with structured formats (e.g., parse trees). Improved word spaces (word-embeddings) on the other hand proved to be efficient in comprehending fine-grained semantic regularities. In this paper, a fine-grained sentiment rating of online reviews based on Deep-RNN is proposed. The performance of the proposed model is evaluated through the conduction of experiments over Stanford sentiment treebank (SST) dataset. Furthermore, the effect of tuning hyper-parameters on the performance of the network is studied. The experimental results reveal that Deep-RNN exhibits better prediction accuracy compared to the traditional shallow counterparts.
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Wadawadagi, R., Pagi, V. (2021). Fine-Grained Sentiment Rating of Online Reviews with Deep-RNN. In: Chiplunkar, N.N., Fukao, T. (eds) Advances in Artificial Intelligence and Data Engineering. AIDE 2019. Advances in Intelligent Systems and Computing, vol 1133. Springer, Singapore. https://doi.org/10.1007/978-981-15-3514-7_52
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DOI: https://doi.org/10.1007/978-981-15-3514-7_52
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