Open AccessA Multi-source Data Fusion Analysis Model in the Research on Public Sentiment Index
Author(s) -
Yunya Zhu,
Zhengang Zhai,
Bingtao Gao,
Song Xu,
Nannan Wu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/7/072018
Subject(s) - livelihood , government (linguistics) , big data , computer science , sentiment analysis , data science , index (typography) , service (business) , sensor fusion , public service , data mining , data modeling , artificial intelligence , business , marketing , world wide web , public relations , database , political science , ecology , philosophy , linguistics , biology , agriculture
At present, China’s economy is continuously developing and making progress. The deep integration of society and big data artificial intelligence and other technologies is constantly promoting the construction of new smart cities. The major livelihood service systems have provided great convenience to the people. At the same time, they have accumulated massive livelihood data. Mining the value of data to solve people’s main problems has been widely concerned by the government and the community. However, the data structure of each system is different. The data is noisy and the systems cannot be interconnected, which leads to the one-sided and single results of data analysis, and it is impossible to use these data to obtain effective results and conclusions. This paper proposes a Public Sentiment Index that reflects the intensity of public appeals. A new multi-source data fusion analysis model is proposed by using a multi-source data fusion method. By constructing a public sentiment database and a public sentiment index model, it can truly reflect the changes in people’s demands. This research can provide a reliable and scientific basis for the government to improve decision-making capacity and service quality.