Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis

Zhao, Narisa; Gao, Huan; Wen, Xin; Li, Hui

doi:10.1109/access.2021.3052937

Cited by 51 publications

(23 citation statements)

References 28 publications

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“…Considering the application environment of financial abnormal data monitoring and analysis, the system adopts BP neural network with mature technology, ideal adaptability, and realization as the identification algorithm [18,19]. The implementation method is as follows.…”

Section: Realization Of Identification Process and Results Outputmentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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In order to solve the problems of low efficiency, high consumption of human and time resources, and low degree of intelligence in the current financial abnormal data detection system in computerized accounting, this paper proposes a financial abnormal data monitoring and analysis algorithm based on data mining and neural network. The analysis algorithm uses the method of data mining to process the original financial data, remove invalid information, retain valuable information, and standardize the data to solve the problem of large labor and time consumption. Then, neural network-related algorithms are used to identify the anomalies of standardized data, so as to realize the intelligent early warning of financial abnormal data. Compared with the financial audit algorithm in traditional accounting computerization, this algorithm has the advantages of high efficiency, low energy consumption, and high intelligence. The test results show that the classification accuracy of the proposed algorithm for abnormal data can reach more than 90%. It is proved that the algorithm is effective and improves the efficiency at the same time. The classification error rate of the classifier designed in this paper is 22.5%, and the accuracy rate is 77.5%. Both estimated and actual values represent the number of times, and there is no physical unit. The experiment shows that the main reason for the error rate is the delay of the inspection results of financial abnormalities. Through the example analysis, it can be concluded that the proposed intelligent analysis method of financial abnormal data based on deep learning has good effectiveness and accuracy and has a certain practical value.

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Section: Realization Of Identification Process and Results Outputmentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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“…Therefore, the research on new English text semantic feature extraction and its understanding methods will not only help further solve a series of key problems in the field of artificial intelligence such as current text classification, machine translation, automatic question answering, text generation, and human-computer interaction but also help facilitate communication and understanding between different languages [ 2 ]. At the same time, with the maturity of key technologies of artificial intelligence such as natural language processing, automated semantic understanding of English texts can quickly understand the international situation, grasp the direction of international public opinion, and protect national information security [ 3 , 4 ]. Therefore, with the advancement of natural language processing in the direction of natural language understanding, solving the hot and difficult problems of text semantic feature extraction and semantic understanding methods in natural language understanding will definitely play an important role in the development of natural language understanding research [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Adaptive Neural Network Algorithm Model in English Text Analysis

Hung

Hsiao

2022

Computational Intelligence and Neuroscience

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Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be seen that AEDR-Adadelta acquires the learning rate dynamically and adaptively. At the same time, by proposing an adaptive exponential decay rate, the number and method of configuring hyperparameters can be reduced, and different learning rates can be effectively obtained for different parameters. The model is based on the encoder-decoder structure and adopts a dual-encoder structure. The transformer encoder is used to extract the context information of the sentence; the Bi-GRU encoder is used to extract the information of the source sentence; and the gated structure is used at the decoder side. The input information is integrated, and each part is matched with different attention mechanisms, which improves the model’s ability to extract and analyze relevant features in sentences. In order to accurately capture the coherence features in English texts, an improved subgraph matching algorithm is used to mine frequently occurring subgraph patterns in sentence semantic graphs, which are used to simulate the unique coherence patterns in English texts, and then analyze the overall coherence of English texts. According to the frequency of occurrence of different subgraph patterns in the sentence semantic graph, the subgraphs are filtered to generate frequent subgraph sets, and the subgraph frequency of each frequent subgraph is calculated separately. The overall coherence quality of English text is quantitatively analyzed by extracting the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graph. According to the experimental results, the algorithm using the adaptive mechanism can reduce the error of the training set and the test set, improve the classification accuracy to a certain extent, and has a faster convergence speed and better text generalization ability. The semantic coherence diagnosis model of English text in this paper performs well in various tasks and has a good effect on improving the automatic correction system of English composition and providing reference for English teachers’ composition correction.

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“… ITIAD [ 38 ]: The method processes the common features of the source domain and the target domain, and applies these features to perform cross-domain sentiment classification. CGRU [ 39 ]: This method is a combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), utilizing the local features generated by CNN and the long-term dependency learned by GRU. …”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…CGRU [ 39 ]: This method is a combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), utilizing the local features generated by CNN and the long-term dependency learned by GRU.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain Text

Liu

Jian-hua

2022

Computational Intelligence and Neuroscience

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Cross-domain text sentiment analysis is a text sentiment classification task that uses the existing source domain annotation data to assist the target domain, which can not only reduce the workload of new domain data annotation, but also significantly improve the utilization of source domain annotation resources. In order to effectively achieve the performance of cross-domain text sentiment classification, this paper proposes a BERT-based aspect-level sentiment analysis algorithm for cross-domain text to achieve fine-grained sentiment analysis of cross-domain text. First, the algorithm uses the BERT structure to extract sentence-level and aspect-level representation vectors, extracts local features through an improved convolutional neural network, and combines aspect-level corpus and sentence-level corpus to form a sequence sentence pair. Then, the algorithm uses domain adversarial neural network to make the feature representation extracted from different domains as indistinguishable as possible, that is, the features extracted from the source domain and the target domain have more similarity. Finally, by training the sentiment classifier on the source domain dataset with sentiment labels, it is expected that the classifier can achieve a good sentiment classification effect in both source and target domain, and achieve sentence-level and aspect-level sentiment classification. At the same time, the error pooled values of the sentiment classifier and the domain adversary are passed backwards to realize the update and optimization of the model parameters, thereby training a model with cross-domain analysis capability. Experiments are carried out on the Amazon product review dataset, and accuracy and F1 value are used as evaluation indicators. Compared with other classical algorithms, the experimental results show that the proposed algorithm has better performance.

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Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis

Cited by 51 publications

References 28 publications

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Application of Adaptive Neural Network Algorithm Model in English Text Analysis

A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain Text

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