TY - GEN
T1 - Web3.0 Literary Landscape
T2 - 33rd International Conference on Computer Communications and Networks, ICCCN 2024
AU - Huang, Sida
AU - Tan, Jialuoyi
AU - Dong, Yuji
AU - Zhang, Jie
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This research introduces a cutting-edge Web3 literary analysis platform, harnessing the power of blockchain and deep learning technologies. By employing the immutable and transparent nature of blockchain, the platform ensures robust copyright protection while offering readers enhanced interactive features. It applies deep learning techniques for comprehensive analyses of sentiment, topic, and stylistic elements, which are instrumental in predicting potential Nobel Prize laureates. This methodology not only enhances the accuracy of predictions but also sheds light on the evaluation criteria and historical trends associated with the Nobel Prize. Moreover, the platform adopts a directed graph model alongside the struc2vec algorithm to create text vectors for comparative studies, uncovering similarities between works that have won awards and those that have been nominated. Utilizing the LESS model for detailed content examination, the platform delves into sequence relationships within semantic networks, thus improving interpretability and visualization. The integration of blockchain technology guarantees access to unbiased datasets, enabling more precise literary analyses and predictions. This innovative approach has been validated using works that have either won or been nominated for the Nobel Prize, proving its efficacy in identifying the textual characteristics favored by the Nobel Prize committee.
AB - This research introduces a cutting-edge Web3 literary analysis platform, harnessing the power of blockchain and deep learning technologies. By employing the immutable and transparent nature of blockchain, the platform ensures robust copyright protection while offering readers enhanced interactive features. It applies deep learning techniques for comprehensive analyses of sentiment, topic, and stylistic elements, which are instrumental in predicting potential Nobel Prize laureates. This methodology not only enhances the accuracy of predictions but also sheds light on the evaluation criteria and historical trends associated with the Nobel Prize. Moreover, the platform adopts a directed graph model alongside the struc2vec algorithm to create text vectors for comparative studies, uncovering similarities between works that have won awards and those that have been nominated. Utilizing the LESS model for detailed content examination, the platform delves into sequence relationships within semantic networks, thus improving interpretability and visualization. The integration of blockchain technology guarantees access to unbiased datasets, enabling more precise literary analyses and predictions. This innovative approach has been validated using works that have either won or been nominated for the Nobel Prize, proving its efficacy in identifying the textual characteristics favored by the Nobel Prize committee.
KW - Blockchain
KW - Deep learning
KW - Semantic analysis
KW - Sequence mining
KW - struc2vec
KW - Web 3.0
UR - http://www.scopus.com/inward/record.url?scp=85203298177&partnerID=8YFLogxK
U2 - 10.1109/ICCCN61486.2024.10637587
DO - 10.1109/ICCCN61486.2024.10637587
M3 - Conference Proceeding
AN - SCOPUS:85203298177
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2024 - 2024 33rd International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 July 2024 through 31 July 2024
ER -