TY - GEN
T1 - Automatic generation of review content in specific domain of social network based on RNN
AU - Tai, Yu
AU - He, Hui
AU - Zhang, Wei Zhe
AU - Jia, Yanguo
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/16
Y1 - 2018/7/16
N2 - The online social network has become a favorable site where a large number of malicious netizens spread rumors and conduct malignant competition. In this paper, we set up a method for generating review content in specific domain of social networks, which uses a recurrent neural network model to generate the social network-style review. Taking Twitter platform as an example platform, we firstly classify the review text according to the sentence pattern; secondly, aiming at different categories, we design corresponding recurrent neural network model to generate the initial review text corresponding to sentence structures; finally, we conduct automatic replacement of the generated initial text through the relevance of subject terms to achieve the effect of better adapting to hot topics. This method is not only easy to operate and economical, but also can evade the most advanced detectors. In the same environment, it is superior to the existing technology and generates more than 85.2% of the output text with correct grammar and wise contents.
AB - The online social network has become a favorable site where a large number of malicious netizens spread rumors and conduct malignant competition. In this paper, we set up a method for generating review content in specific domain of social networks, which uses a recurrent neural network model to generate the social network-style review. Taking Twitter platform as an example platform, we firstly classify the review text according to the sentence pattern; secondly, aiming at different categories, we design corresponding recurrent neural network model to generate the initial review text corresponding to sentence structures; finally, we conduct automatic replacement of the generated initial text through the relevance of subject terms to achieve the effect of better adapting to hot topics. This method is not only easy to operate and economical, but also can evade the most advanced detectors. In the same environment, it is superior to the existing technology and generates more than 85.2% of the output text with correct grammar and wise contents.
KW - Recurrent neural network
KW - Social network
KW - Text categorization
KW - Text generation
UR - https://www.scopus.com/pages/publications/85051046416
U2 - 10.1109/DSC.2018.00096
DO - 10.1109/DSC.2018.00096
M3 - 会议稿件
AN - SCOPUS:85051046416
T3 - Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018
SP - 601
EP - 608
BT - Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018
Y2 - 18 June 2018 through 21 June 2018
ER -