TY - GEN
T1 - Analysis of literary based on deep emotional network
AU - Gong, Lejun
AU - Tang, Xiangyu
AU - Li, Huakang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Literary works are mainly reviewed by professional judges, whose evaluation and analysis are limited by their own experiences and preferences, which makes it difficult to find the value of the works. We design a method to analyze the quality of literary works based on deep characters emotional network. Taking the representative works of Nobel Prize in Literature and nominees for literature as the research objects, we extract the entity relationship network. The node is the entity in the works, and the edge is the relationship between entities. With the help of graph embedding algorithm, we analyze the similarities and differences of different works in emotional distribution of characters. The results show that the works of the Nobel Prize in Literature winners in 2012 and 2013 have the characteristics of high concentration of characters and even emotion fluctuation.
AB - Literary works are mainly reviewed by professional judges, whose evaluation and analysis are limited by their own experiences and preferences, which makes it difficult to find the value of the works. We design a method to analyze the quality of literary works based on deep characters emotional network. Taking the representative works of Nobel Prize in Literature and nominees for literature as the research objects, we extract the entity relationship network. The node is the entity in the works, and the edge is the relationship between entities. With the help of graph embedding algorithm, we analyze the similarities and differences of different works in emotional distribution of characters. The results show that the works of the Nobel Prize in Literature winners in 2012 and 2013 have the characteristics of high concentration of characters and even emotion fluctuation.
KW - Named Entity Recognition
KW - Network Learning
KW - Relational network
UR - http://www.scopus.com/inward/record.url?scp=85116665649&partnerID=8YFLogxK
U2 - 10.1109/BigCom53800.2021.00001
DO - 10.1109/BigCom53800.2021.00001
M3 - Conference Proceeding
AN - SCOPUS:85116665649
T3 - Proceedings - 2021 7th International Conference on Big Data Computing and Communications, BigCom 2021
SP - 227
EP - 233
BT - Proceedings - 2021 7th International Conference on Big Data Computing and Communications, BigCom 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Big Data Computing and Communications, BigCom 2021
Y2 - 13 August 2021 through 15 August 2021
ER -