A Sentiment Classification Model Based on Deep Learning

Liu, Zijia; Tao, Ran; Shi, Youqun; Luo, Qinglan

doi:10.1007/978-3-030-81635-3_32

Cited by 2 publications

(1 citation statement)

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“…Many NLP tasks use convolutional neural networks to extract local semantic information. For example, (Liu et al, 2021) used CNN with multi‐scale convolution kernels to extract rich local critical semantic information from Weibo text and e‐commerce reviews in sentiment classification tasks. (Javed et al, 2021) used CNN to extract local critical semantic information from online reviews to identify false opinions.…”

Section: Related Workmentioning

confidence: 99%

NeuralConflict: Using neural networks to identify norm conflicts in normative documents

Huang

Sun

2022

Expert Systems

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A large number of norms, which express constraints on people's behaviour within a specific range, are contained in normative documents. In writing and revising normative documents, conflicts between two norms often arise. The task Norm Conflict Identification (NCI) aims to identify such conflicts. The existing NCI methods based on statistical learning are all pipelines, which cause errors to accumulate, and cannot sufficiently extract helpful information. According to the characteristics of NCI, we propose a neural network model called NeuralConflict. This end‐to‐end model can avoid the accumulation of errors and makes it easier to obtain the optimal global solution. The model sets up an auxiliary task to predict whether two norms are semantically related and shares part of the information with the task NCI. In addition, the model uses a convolutional neural network with differently sized convolution kernels to extract local semantic information from the norms. Finally, the model inputs the shared and local semantic information into a fully connected neural network to predict whether the two norms conflict. We construct a Chinese dataset and use it with an existing English dataset for the experiments. Experimental results show that NeuralConflict achieves optimum results on both datasets.

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Section: Related Workmentioning

confidence: 99%