Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

Chen, Yan; Yao, Lina; Guo-xiong, Xiang; Zhang, Guanghe; Tang, Ting; Zhong, Linhui

doi:10.1109/access.2020.3005823

Cited by 90 publications

(38 citation statements)

References 37 publications

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“…However, a single GRU can only calculate the information at the next time based on the information at the last time and cannot calculate the information at the last time based on the information at the next time. The bidirectional gate recurrent unit (BiGRU) adds a reverse GRU based on the single sequential GRU, which combines the forward GRU and the reverse GRU to capture the contextual semantic information between texts ( Cheng et al, 2020 ). Therefore, this paper uses BiGRU to better capture sentence global semantic information.…”

Section: Methodsmentioning

confidence: 99%

“…Convolutional neural networks was first applied in the field of computer vision, and in recent years has been gradually applied to NLP tasks and has achieved good processing results ( Cheng et al, 2020 ). CNN is mainly composed of the input layer, the convolutional layer, the pooling layer, and the output layer.…”

Section: Methodsmentioning

confidence: 99%

“…BiGRU is good at modeling sequence data and can establish an effective text representation through the long-term dependence of learning time features and sentences. It is successfully applied to NLP tasks, but local features of the text cannot be better extracted ( Cheng et al, 2020 ). CNN has been proven to be able to learn most local features from natural language and has achieved good results in sentence classification ( Zheng and Zheng, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

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A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom’s Taxonomy of Cognition Objectives Perspective

Chen

Cai

Chen³

et al. 2021

Front. Psychol.

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The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom’s Taxonomy of Cognition Objectives Perspective

Chen

Cai

Chen³

et al. 2021

Front. Psychol.

Self Cite

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“…With the rise and application of deep learning, many researchers have begun to use deep learning to solve emotion classification problems. Among them, multilayer perceptron (MLP) [12], CNN [13], RNN [14], attention mechanism [15][16][17], and other neural network structures are widely used in text sentiment analysis tasks and can get a better semantic representation of sentences.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Sentiment Analysis Method of Network Text Based on Improved AT-BiGRU Model

Zhang

2021

Scientific Programming

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In order to solve the problems existing in the current method of emotional analysis of network text, such as long training time, complex calculation, and large space cost, this paper proposes an Internet text sentiment analysis method based on the improved AT-BiGRU model. Firstly, the textblob package is imported to correct spelling errors before text preprocessing. Secondly, pad_sequences are used to fill in the input layer with a fixed length, the two-way gated recurrent network is used to extract information, and the attention mechanism is used to highlight the key information of the word vector. Finally, the GNU memory unit is transformed, and an improved BiGRU that can adapt to the recursive network structure is constructed. The proposed model is experimentally demonstrated on the SemEval-2014 Task 4 and SemEval-2017 Task 4 datasets. Experimental results show that the proposed model can effectively avoid the text sentiment analysis bias caused by spelling errors and prove the effectiveness of the improved AT-BiGRU model in terms of accuracy, loss rate, and iteration time.

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“…When it comes to the recognition and classification of financial instruments, the CNN also has super-strong control capability of the processing accuracy [10]. Therefore, this paper designs a CNN for the classification of financial instruments.…”

Section: Introductionmentioning

confidence: 99%

An Image Classification Algorithm of Financial Instruments Based on Convolutional Neural Network

Dong¹,

Li²

2020

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The boom of global economy has caused an explosive growth in the issuance and use of financial instruments. Traditionally, the financial instruments are recognized and classified manually, which increases the burden of financial staff and consumes lots of financial time. To solve the problems, this paper designs a convolutional neural network (CNN) for classification of financial instruments, covering components like traditional CNN, shallow convolutional layers, and cropping structure. Then, the momentum weight update was combined with weight attenuation to accelerate the model learning. In addition, the authors designed a preprocessing method for rapid pixel-level adjustment of financial instruments, enabling the proposed CNN to classify financial instruments of various sizes. Experiments show that our CNN can identify various financial instruments, and classify them at an accuracy as high as 96%.

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Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

Cited by 90 publications

References 37 publications

A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom’s Taxonomy of Cognition Objectives Perspective

A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom’s Taxonomy of Cognition Objectives Perspective

Sentiment Analysis Method of Network Text Based on Improved AT-BiGRU Model

An Image Classification Algorithm of Financial Instruments Based on Convolutional Neural Network

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