Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis
Ieee Transactions on Computational Social Systems
; : 10, 2022.
Artigo
em Inglês
| Web of Science | ID: covidwho-1861140
ABSTRACT
This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitter platform's user communities. Each community is trained with the embedded deep learning model. To recommend hashtags for the new user in the social network, the correlation between the tweets of such user and the knowledge graph of each community is explored to set the relevant communities of such user. The models of the relevant communities are used to infer the hashtags of the tweets of such users. We conducted extensive testing to demonstrate the usefulness of our methods on a variety of tweet collections. Experimental results show that the proposed approach is more efficient than the baseline approaches in terms of both runtime and accuracy.
Texto completo:
Disponível
Coleções:
Bases de dados de organismos internacionais
Base de dados:
Web of Science
Idioma:
Inglês
Revista:
Ieee Transactions on Computational Social Systems
Ano de publicação:
2022
Tipo de documento:
Artigo
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