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

Lu, Xinxin; Zhang, Hong

doi:10.1155/2021/6669664

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…rough the framework of the online education resource integration, the distributed education resource, database, and education resource integration service center were organically interconnected, forming a co-construction and sharing mode of online education resources with intensive management and distributed storage, thus achieving the best solution of resource construction, resource sharing, and resource application [6]. Lu proposed the integration of online education resources based on three kinds of social software: Blog, Wiki, and Model, and analyzed their respective integration methods, strategies, and characteristics [7]. Tang and T discussed the integration and integration modes of online education resources and put forward three integration modes, namely, the education resource management database mode, the education resource center mode, and the distributed education resource network mode.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

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The key sentimental words in the text cannot be paid attention to effectively, and language knowledge such as the text information and the sentimental resources are relied on. Therefore, it is necessary to make full use of this unique sentimental information to achieve the best performance of the model. In order to solve the problems, a method based on the fusion of the convolutional neural network and the bidirectional GRU network text sentiment analysis capsule model to analyze the ideological and political education of public opinion is put forward. In this model, each sentiment category is combined with the attention mechanism to generate feature vectors to construct sentiment capsules. Finally, the text sentiment categories are judged according to the attributes of the capsules. The model is tested on MR, IMDB, SST-5, and the data set of the ideological and political education review. Experimental results show that compared with MC-CNN-LSTM, the readiness rate of the proposed model is improved by 5.1%, 2.8%, 2.8%, and 1.6% on four public Chinese and English data sets, respectively. Compared with LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, the accuracy of the proposed MC-BiGRU-Capsule model on MR and SST-5 data are 3.2%, 2.4%, and 3.4% higher than that of the LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, respectively. It also shows a better classification effect on multiclassification data sets. It is concluded that compared with other baseline models, this method has a better classification effect.

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Section: Literature Reviewmentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

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“…However, the CNN model ignores the correlation between the whole and the part, and the analysis accuracy of comment text needs to be further improved. Reference [ 18 ] proposed a network text emotion analysis method based on the improved attention mechanism and two-way gated loop unit model. Reference [ 19 ] proposed a text emotion analysis method combining dictionary language model and deep learning to solve the problem of accurate and rapid emotion analysis of comment text in the network big data environment.…”

Section: Introductionmentioning

confidence: 99%

Emotion Analysis Model of Microblog Comment Text Based on CNN-BiLSTM

Yi²

2022

Computational Intelligence and Neuroscience

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Aiming at the problems of over reliance on labor and low generalization of traditional emotion analysis methods based on dictionary and machine learning, an emotion analysis model of microblog comment text based on deep learning is proposed. Firstly, text is obtained through microblog crawler program. After data preprocessing, including data cleaning, Chinese word segmentation, removal of stop words, and so on, the Skip-gram model is used for word vector training on a large-scale unmarked corpus, and then the trained word vector is used as the text input of CNN-BiLSTM model, which combines Bidirectional Long-Short Term Memory (BiLSTM) neural network and Convolution Neural Network (CNN). Considering the historical context information and the subsequent context information, BiLSTM can better use the temporal relationship of text to learn sentence semantics. CNN can extract hidden features from the text and combine them. Finally, after Adamax optimization training, the emotion type of microblog comment text is output. The proposed model combines the learning advantages of BiLSTM and CNN. The overall accuracy of text emotion analysis has been greatly improved, with an accuracy of 0.94 and an improvement of 8.51% compared with the single CNN model.

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“…Compared with the traditional model, the accuracy can be improved by 1.7%. Lu et al built an improved BiGRU that can adapt to the recursive network structure [22]. In addition, Textblob technology is used to correct spelling errors during preprocessing, so that the model can well avoid errors caused by spelling errors.…”

Section: The Methods Based On Deep Learningmentioning

confidence: 99%

Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

Wang¹,

Du²,

Shao³

et al. 2022

Preprint

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With the development of online travel services, it has great application prospects to timely mine users' evaluation emotions for travel services and use them as indicators to guide the improvement of online travel service quality. In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the SCCL model based on capsule network and sentiment lexicon. SCCL model aims at the lack of consideration of local features and emotional semantic features of the text in the language model that can efficiently extract text context features like BERT and GRU. Then make the following improvements to their shortcomings. On the one hand, based on BERT-BiGRU, the capsule network is introduced to extract local features while retaining good context features. On the other hand, the sentiment lexicon is introduced to extract the emotional sequence of the text to provide richer emotional semantic features for the model. To enhance the universality of the sentiment lexicon, the improved SO-PMI algorithm based on TF-IDF is used to expand the lexicon, so that the lexicon can also perform well in the field of online travel reviews.

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

Cited by 9 publications

References 25 publications

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Emotion Analysis Model of Microblog Comment Text Based on CNN-BiLSTM

Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

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