Context Aware Sentiment Link Prediction in Heterogeneous Social Network

Zhao, Anping; Yu, Yu

doi:10.1007/s12559-021-09830-z

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Chai et al [33] used the extended hidden Markov model to study the potential time correlation in the public opinion comment data by integrating multi-source information such as news events, comments and emotions, to construct a multi-source heterogeneous data analysis method for predicting future market prices. Anping and Yu [34] proposed a novel heterogeneous social network embedding-based approach for sentiment link prediction that takes both global structural information with multidimensional relations and heterogeneous context information into consideration to leverage rich and intrinsic association information.…”

Section: Emotion Evolution Based On Short Textsmentioning

confidence: 99%

DRMM: A novel data mining-based emotion transfer detecting method for emotion prediction of social media

Shi

Xue

Yin

et al. 2022

Journal of Information Science

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With the progress of the Internet and information technology, emotion analysis has been applied to analyse the emotional orientation and evolution trend of online public opinion of online tweets. At present, most of the existing methods use econometric model and machine learning algorithm to predict the trend of online public opinion. Although these methods have achieved good prediction results, they do not take into account the influence of internal factors on network public opinion prediction, such as mutual migration among emotion classes. The emotion may change dynamically because different events trigger it in the evolution process. In this view, this article proposes a novel method, called Deviation Rule Markov Model (DRMM), to predict the emotional change trend of Internet users in online public opinion by analysing the correlation between Internet users’ emotional categories. Structurally, the proposed DRMM involves various processes such as pre-processing, emotion classification, data mining and transfer prediction. For the processing of network comment data, the proposed model initially undergoes pre-processing to delete unnecessary data. Then, the extended fuzzy emotion ontology is used to annotate the emotion class of the comment data. Besides, an extended association rule mining algorithm is used in the emotion association analysis process to obtain the transfer probability between emotion classes. Moreover, Markov chain is used to construct an emotional state transition matrix to predict the transition probability of positive or negative emotions. According to the predicted single emotion transfer probability results, the analytic hierarchy process is used to assign values to different emotion classes, and finally, the transfer probability of the overall emotion in a certain period is obtained. Compared with the actual case, the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model are 2.7119 and 3.7254, respectively, which has good prediction performance.

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Section: Emotion Evolution Based On Short Textsmentioning

confidence: 99%

DRMM: A novel data mining-based emotion transfer detecting method for emotion prediction of social media

Shi

Xue

Yin

et al. 2022

Journal of Information Science

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“…These models construct homogeneous graphs through meta-paths and learn node representations using traditional GNN (Graph Neural Network) models. HMSN [9] extracts potential relationships between similar node types by utilizing metapaths and similarity. Although these meta-path-based methods achieve some level of success, it is not possible to explore all meta-paths, and the quality of the predefined metapaths significantly influences the algorithm's performance.…”

Section: Introductionmentioning

confidence: 99%

HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention

Zhao,

Miao,

et al. 2024

IEEE Access

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Heterogeneous information networks provide abundant structural and semantic information. Two main strategies for leveraging this data include meta-path-based and meta-path-free methods. The effectiveness of the former heavily depends on the quality of manually defined meta-paths, which may lead to the instability of the model. However, the existing meta-path-free methods lack of neighbor screening during aggregating, and there is also an overemphasis on attribute information. To address these issues, we propose the Heterogeneous Graph Neural Network model by incorporating Quantitative Sampling and Structure-aware Attention. We introduce a Quantitative Sampling Module that calculates the similarity between neighbors of the target nodes and target nodes, enabling us to select the top k nodes with the strongest relevance to the target node based on this measure, and incorporate a Structure-aware Attention module during the aggregation of neighbor information. This module combines both structural and attribute information to aggregate the neighbor information effectively. By implementing these improvements, our proposed model exhibits superior performance compared to several state-of-the-art methods on two realworld datasets.

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Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications

Bing

Yuan

Zhu³

et al. 2022

Artif Intell Rev

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Context Aware Sentiment Link Prediction in Heterogeneous Social Network

Cited by 5 publications

References 32 publications

DRMM: A novel data mining-based emotion transfer detecting method for emotion prediction of social media

DRMM: A novel data mining-based emotion transfer detecting method for emotion prediction of social media

HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention

Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications

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