Name
Title | ||
|---|---|---|
A Method for Predicting the Winner of the USA Presidential Elections using Data extracted from Twitter |
Abstract | ||
|---|---|---|
This paper presents work on using data extracted from Twitter to predict the outcome of the latest USA presidential elections on 8th of November 2016 in three key states: Florida, Ohio and N. Carolina, focusing on the two dominant candidates: Donald J. Trump and Hillary Clinton. Our method comprises two steps: pre-processing and analysis and it succeeded in capturing negative and positive sentiment towards these candidates, and predicted the winner in these States, who eventually won the presidency, when other similar attempts in the literature have failed. We discuss the strengths and weaknesses of our method proposing directions for further work. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/SEEDA-CECNSM.2018.8544919 | 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM) |
Keywords | Field | DocType |
Sentiment analysis,Classification,Social Media,Data mining,Artificial Intelligence and Applications | Presidential system,Political science,Voting,Sentiment analysis,Public relations,Presidency,Strengths and weaknesses,Market research | Conference |
ISBN | Citations | PageRank |
978-1-5386-4351-8 | 2 | 0.47 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lazaros Oikonomou | 1 | 2 | 0.47 |
| Christos Tjortjis | 2 | 173 | 24.40 |