Graph Domain Adversarial Transfer Network for Cross-Domain Sentiment Classification

Tang, Hengliang; Mi, Yuan; Xue, Fei; Cao, Yang

doi:10.1109/access.2021.3061139

Cited by 20 publications

(6 citation statements)

References 22 publications

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“…Transfer learning, whose core is to achieve the purpose of assisting the learning process in the target domain by finding the similarity between the source domain and target domain, has shown great effectiveness in many fields [54][55][56]. Transfer learning provides a fruitful approach for learning from existing knowledge in the original domain, applying it to new domain based on the similarity of the datasets.…”

Section: Discussionmentioning

confidence: 99%

Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

Han,

Wang,

Wang

et al. 2023

Sustainability

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As a crucial issue in renewable energy, accurate prediction of direct normal solar irradiance (DNI) is essential for the stable operation of concentrated solar power (CSP) stations, especially for those in arid desert areas. In this study, in order to fully explore the laws of climate change and assess the solar resources in arid desert areas, we have proposed a mixed multi-pattern regression model (MMP) for short-term DNI prediction using prior knowledge provided by the clear-sky solar irradiance (CSI) model and time series patterns of key meteorological factors mined using PR-DTW on different time scales. The contrastive experimental results demonstrated that MMP can outperform existing DNI prediction models in terms of three recognized statistical metrics. To address the challenge of limited data in arid desert areas, we presented the T-MMP model involving combined transfer learning and MMP. The experimental results demonstrated that T-MMP outperformed MMP in DNI prediction by exploiting the significant correlation between meteorological time series patterns in similar areas for data augmentation. Our study provided a valuable prediction model for accurate DNI prediction in arid desert areas, facilitating the economical and stable operation of CSP plants.

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Section: Discussionmentioning

confidence: 99%

Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

Han,

Wang,

Wang

et al. 2023

Sustainability

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“…FP net is a neural network structure that enhances the effect of text classification. It is mainly implemented by using the gradient reversal network, which uses the Gradient Reversal Layer (GRL) to extract the common features of multiple categories [13]. The implementation principle of GRL is introduced in detail and it is used to extract common features in Domain Adaptation.…”

Section: Fp Netmentioning

confidence: 99%

Feature Mining of News Communication Topic Elements Based on BERT Model

Zheng

2023

Sociology

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In order to solve the problems of lack of standardization, fuzzy semantics and sparse features in news topic texts, a feature mining of news communication topic elements based on the BERT model is proposed. In the research, multi-layer fully connected layer feature extraction is performed on the output of the news topic text in the BERT model, and the final extracted text features are purified by the feature projection method to enhance the classification effect.Then the feature projection network is fused in the hidden layer inside the BERT model for feature projection, so as to enhance and purify the classification features through the feature projection of the hidden layer. Experiments are performed on Toutiao, Sohu News, THUC News-L, and THUC News-S datasets. The experimental results show that compared with the baseline BERT method, the two methods have better performance in terms of accuracy and macro-average F1 value, and the highest accuracy is 86. 96%, 86. 17%, 94. 40% and 93.73%, respectively, which verifies the feasibility and effectiveness of the proposed method. It is concluded that the proposed method for news topic text classification combining BERT and FP net is effective and efficient.

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“…Tang et al [ 27 ] proposed Graph Domain Adversarial Transfer Network (GDATN) for cross-domain sentiment classification using Bidirectional Long Short-Term Memory (BiLSTM) Network and Graph Attention Network (GAT). Shuang et al[ 28 ] created an interactive POS-aware network (IPAN) to improve part of speech-tagging and sentiment classification accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach

Uthirapathy

Domnic

2022

Int. j. inf. tecnol.

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Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately. This study proposes a new framework for analysing both information diffusion and opinion evolution. The change in opinion over time is known as opinion evolution. To propose a new model for predicting information diffusion and opinion analysis in social media, a forest fire algorithm, cuckoo search, and fuzzy c-means clustering are used. The forest fire algorithm is used to determine the diffuser and non-diffuser of information in social networks, and fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to cluster Twitter content into various opinion categories and to determine opinion change. On different Twitter data sets, the proposed model outperformed the existing methods in terms of precision, recall, and accuracy.

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Graph Domain Adversarial Transfer Network for Cross-Domain Sentiment Classification

Cited by 20 publications

References 22 publications

Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

Feature Mining of News Communication Topic Elements Based on BERT Model

Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach

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