Name
Title | ||
|---|---|---|
Prediction of the 2017 French Election Based on Twitter Data Analysis Using Term Weighting |
Abstract | ||
|---|---|---|
Twitter is a social network that lets users post their opinions about current affairs, share their social events, and interact with others. It has now become one of the largest sources of news, with over 200 million active users monthly. This paper proposes a method to predict election results based on Twitter data analysis, which analyses sentimental information using term weighting and selection to predict the popularity of candidates. The proposed method was used for predicting the result of the 2017 French presidential election. It has been shown that the proposed method significantly outperformed the Tumasjan’s method and beat another method proposed by the authors in 2017 as well. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CEEC.2018.8674188 | 2018 10th Computer Science and Electronic Engineering (CEEC) |
Keywords | Field | DocType |
Voting,Twitter,Data analysis,Analytical models,Predictive models,Media | Weighting,Social network,Voting,Information retrieval,Presidential election,Computer science,Popularity | Conference |
ISSN | ISBN | Citations |
2472-1530 | 978-1-5386-7275-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Wang | 1 | 401 | 111.60 |
| John Q. Gan | 2 | 18 | 4.87 |