Open AccessForecast Method of Energy Public Opinion Based on Conditional Random Field
Author(s) -
Yu Wang,
Shengzhen Xie,
Junri Tang,
Ling Sui,
Kaiqi Kou,
Zhiqiong Wang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/661/1/012034
Subject(s) - conditional random field , government (linguistics) , public opinion , process (computing) , affect (linguistics) , field (mathematics) , energy (signal processing) , computer science , state (computer science) , psychology , econometrics , artificial intelligence , political science , mathematics , algorithm , statistics , law , operating system , philosophy , linguistics , communication , politics , pure mathematics
As the driving force of development in the new century, the energy problem is an important factor restricting economic development, which is related to a country’s economic security and national security. The public’s views or attitudes towards (heat events related to energy issues, such as the State Grid’s repeated public attention during the 2016 electricity reform) significantly affect the government’s monitoring and guidance of the oil crisis. Ordinary research on emotion prediction regards the change of public attitude towards events as a random process, only considering the influence of the attitude tendency held by users in the early stage on the later stage of emotion development, and not considering the influence of external factors on emotion prediction in the actual situation. This paper proposes a public opinion emotion prediction method based on conditional random fields. Taking the transition probability between external factors as the characteristic transition matrix of the conditional random field, considering the influence of external factors on the development of events, and combining with bidirectional LSTM, the BILSTM-CRF model in this paper is proposed. Experiments show that the emotion prediction method based on conditional random fields can predict the emotion state more accurately, which verifies the superiority of the emotion prediction method based on conditional random fields.