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Article

SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network

School of Computer Science and Technology, Dalian University of Technology, Ganjingzi District, Dalian 116024, China
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Academic Editors: Ida Mele and Luis Martínez López
Received: 23 July 2021 / Revised: 7 September 2021 / Accepted: 9 September 2021 / Published: 22 September 2021
(This article belongs to the Special Issue Recommendation Algorithms and Web Mining)
Hashtags are considered important in various real-world applications, including tweet mining, query expansion, and sentiment analysis. Hence, recommending hashtags from tagged tweets has been considered significant by the research community. However, while many hashtag recommendation methods have been developed, finding the features from dictionary and thematic words has not yet been effectively achieved. Therefore, we developed an effective method to perform hashtag recommendations, using the proposed Sine Cosine Political Optimization-based Deep Residual Network (SC-Political ResNet) classifier. The developed SCPO is designed by integrating the Sine Cosine Algorithm (SCA) with the Political Optimizer (PO) algorithm. Employing the parametric features from both, optimization can enable the acquisition of the global best solution, by training the weights of classifier. The hybrid features acquired from the keyword set can effectively find the information of words associated with dictionary, thematic, and more relevant keywords. Extensive experiments are conducted on the Apple Twitter Sentiment and Twitter datasets. Our empirical results demonstrate that the proposed model can significantly outperform state-of-the-art methods in hashtag recommendation tasks. View Full-Text
Keywords: deep learning; hashtag recommendation; long short-term memory; natural language processing; sine cosine algorithm; text classification; tweet modeling; word embedding deep learning; hashtag recommendation; long short-term memory; natural language processing; sine cosine algorithm; text classification; tweet modeling; word embedding
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MDPI and ACS Style

Banbhrani, S.K.; Xu, B.; Liu, H.; Lin, H. SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network. Information 2021, 12, 389. https://0-doi-org.brum.beds.ac.uk/10.3390/info12100389

AMA Style

Banbhrani SK, Xu B, Liu H, Lin H. SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network. Information. 2021; 12(10):389. https://0-doi-org.brum.beds.ac.uk/10.3390/info12100389

Chicago/Turabian Style

Banbhrani, Santosh K., Bo Xu, Haifeng Liu, and Hongfei Lin. 2021. "SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network" Information 12, no. 10: 389. https://0-doi-org.brum.beds.ac.uk/10.3390/info12100389

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