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- W4387090419 abstract "Short text emotion analysis has always been a key area of concern for mastering hot public opinion and understanding public evaluation. However, due to the complex factors involved in emotion analysis such as polysemy, irony, word sequence dependency, the accuracy of some existing short text emotion analysis methods still has room for improvement. Based on this, this paper proposes a short text sentiment analysis method based on multi-scale improved CNN model under Tensorflow. This method builds multi-scale convolution layer on the basis of the original convolution layer to help the model mine more potential function information, and at the same time imposes a lightweight constraint on the network to reduce the probability of over-fitting. Finally, attention mechanism is introduced to give different weights to features according to their importance, and text emotion analysis is carried out. The experimental results show that the average loss value of the improved CNN model is reduced by 38.7%, and the average accuracy is improved by 21.6%." @default.
- W4387090419 created "2023-09-28" @default.
- W4387090419 creator A5008659449 @default.
- W4387090419 date "2023-08-11" @default.
- W4387090419 modified "2023-10-18" @default.
- W4387090419 title "Emotional Analysis of Short Text Based on Multiscale Improved CNN Model Under Tenorflow" @default.
- W4387090419 doi "https://doi.org/10.1109/icipca59209.2023.10258004" @default.
- W4387090419 hasPublicationYear "2023" @default.
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