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- W3188289952 abstract "People have created a massive number of opinion content on the Web to support their opinions about goods and services that have a high commercial value in their everyday lives. For polarization, these remark sentences sometimes contain multiple comment components with varying sentiments, rendering the actual purpose of the sentence irrelevant. The goal of aspect-oriented sentiment analysis is just to identify the target’s perception polarities in scenario. Deep learning definitely is maturing, and it’s becoming more common to use deep learning approaches to identify emotions. The use of the convolutional neural network & the bidirectional gated recurrent device in a sentiment classification model is presented. LSTM is equivalent to BiGRU which is a time cyclic neural network with reduced processing complexity. We propose that CNN and bi-GRU be used in parallel, with weights applied to elements of the CNN and bi-GRU output. We use CNN and bi-GRU in parallel because CNN may produce more significant feature space from the original dataset than the bi-GRU components used as the CNN input. For the final sentiment categorization, the Sigmoid classifier is utilised." @default.
- W3188289952 created "2021-08-16" @default.
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- W3188289952 date "2021-06-25" @default.
- W3188289952 modified "2023-10-01" @default.
- W3188289952 title "Aspect Based Opinion Mining Leveraging Weighted BiGRU and CNN Module in Parallel" @default.
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- W3188289952 doi "https://doi.org/10.1109/conit51480.2021.9498441" @default.
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