News Feature Extraction for Events on Social Network Platforms

Jin, Peiquan; Mu, Lin; Zheng, Lizhou; Zhao, Jie; Yue, Lihua

doi:10.1145/3041021.3054151

Cited by 21 publications

(5 citation statements)

References 21 publications

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“…To further verify the effectiveness of the method in this paper, it is compared with the BBW [28] algorithm proposed by Zhang. is algorithm extracts the burst words based on the improved TF-IDF to calculate the base weight and burst weight in each time window, and then clusters to detect burst events based on the obtained burst words, which is a more classical algorithm; compared with the Burst_st algorithm proposed by [22], which is a more efficient algorithm among the current feature-centered methods, this algorithm combines sentiment features with microbial topic tags to detect the breaking events, which has some comparability.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…e MCMF-A fusion layer can merge the output of the features from multiple channels to generate multifeature vectors, which can then exploit the multilayer semantic information of the microblogging text. Specifically, firstly, local features are obtained by the CNN iterative computation; secondly, global features are obtained by combining the semantic information with BiLSTM; finally, an attention mechanism is introduced for secondary extraction to retain the important features [28].…”

Section: Attention Mechanism Layermentioning

confidence: 99%

See 1 more Smart Citation

A Multichannel Model for Microbial Key Event Extraction Based on Feature Fusion and Attention Mechanism

Wang

2021

Security and Communication Networks

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In order to further mine the deep semantic information of the microbial text of public health emergencies, this paper proposes a multichannel microbial sentiment analysis model MCMF-A. Firstly, we use word2vec and fastText to generate word vectors in the feature vector embedding layer and fuse them with lexical and location feature vectors; secondly, we build a multichannel layer based on CNN and BiLSTM to extract local and global features of the microbial text; then we build an attention mechanism layer to extract the important semantic features of the microbial text; thirdly, we merge the multichannel output in the fusion layer and use soft; finally, the results are merged in the fusion layer, and a surtax function is used in the output layer for sentiment classification. The results show that the F1 value of the MCMF-A sentiment analysis model reaches 90.21%, which is 9.71% and 9.14% higher than the benchmark CNN and BiLSTM models, respectively. The constructed dataset is small in size, and the multimodal information such as images and speech has not been considered.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Attention Mechanism Layermentioning

confidence: 99%

A Multichannel Model for Microbial Key Event Extraction Based on Feature Fusion and Attention Mechanism

Wang

2021

Security and Communication Networks

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“…Thus, people can expand their network in society and finally form a big social network. Generally, social networks can provide a number of new applications and services, such as news feature exploration [7], event monitoring [8,9], and sentiment analysis of reviews [10].…”

Section: Knowledge Sharing In Social Networkmentioning

confidence: 99%

User Willingness toward Knowledge Sharing in Social Networks

et al. 2018

Self Cite

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Social networks introduce new potential for people to share knowledge with others. However, it is not clear what factors influence user willingness toward knowledge sharing in social networks. Aiming to answer these questions, in this paper we analyze the major factors influencing user willingness toward knowledge sharing in social networks and propose a new research model that is inspired by the technology acceptance model (TAM). In particular, we introduce a new independent variable called perceived value which is described by four aspects: social value, entertainment value, emotion value, and information value. In addition, we introduce a new mediating variable, trust, to reflect the intermediating relationship between perceived value and knowledge-sharing willingness. We conduct an empirical analysis on questionnaire data and present comprehensive results on reliability and validity, factor analysis, correlation analysis, and mediating effects analysis. The results show that perceived value has a significant impact on knowledge-sharing willingness, and trust plays a partial intermediate role between perceived value and knowledge-sharing willingness. Further, we present some research implications for knowledge sharing and learning innovation in social networks, as well as some suggestions for organizations to advance knowledge sharing and learning innovation in the social-network age.

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“…In the future, we will consider integrating social network tools with the teaching of management information systems courses [9,10]. Another research direction is to develop Web-based systems that can automatically crawl teaching resources from the Web and form a standard database of teaching materials [11].…”

Section: Discussionmentioning

confidence: 99%