loading
Papers

Research.Publish.Connect.

Paper

Authors: Parnian Kassraie 1 ; Alireza Modirshanechi 1 and Hamid K. Aghajan 2

Affiliations: 1 Sharif University of Technology, Iran, Islamic Republic of ; 2 Sharif University of Technology and University of Gent, Iran, Islamic Republic of

ISBN: 978-989-758-255-4

Keyword(s): Social Media Text Mining, Sentiment Analysis, Google Trends, Twitter, Election Prediction, Gaussian Process Regression.

Abstract: It is common to use online social content for analyzing political events. Twitter-based data by itself is not necessarily a representative sample of the society due to non-uniform participation. This fact should be noticed when predicting real-world events from social media trends. Moreover, each tweet may bare a positive or negative sentiment towards the subject, which needs to be taken into account. By gathering a large dataset of more than 370,000 tweets on 2016 US Elections and carefully validating the resulting key trends against Google Trends, a legitimate dataset is created. A Gaussian process regression model is used to predict the election outcome; we bring in the novel idea of estimating candidates’ vote shares instead of directly anticipating the winner of the election, as practiced in other approaches. Applying this method to the US 2016 Elections resulted in predicting Clinton’s majority in the popular vote at the beginning of the elections week with 1% error. The high va riance in Trump supporters’ behavior reported elsewhere is reflected in the higher error rate of his vote share. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.80.6.254

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kassraie, P.; Modirshanechi, A. and Aghajan, H. (2017). Election Vote Share Prediction using a Sentiment-based Fusion of Twitter Data with Google Trends and Online Polls.In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: KDCloudApps, ISBN 978-989-758-255-4, pages 363-370. DOI: 10.5220/0006484303630370

@conference{kdcloudapps17,
author={Parnian Kassraie. and Alireza Modirshanechi. and Hamid K. Aghajan.},
title={Election Vote Share Prediction using a Sentiment-based Fusion of Twitter Data with Google Trends and Online Polls},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: KDCloudApps,},
year={2017},
pages={363-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006484303630370},
isbn={978-989-758-255-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: KDCloudApps,
TI - Election Vote Share Prediction using a Sentiment-based Fusion of Twitter Data with Google Trends and Online Polls
SN - 978-989-758-255-4
AU - Kassraie, P.
AU - Modirshanechi, A.
AU - Aghajan, H.
PY - 2017
SP - 363
EP - 370
DO - 10.5220/0006484303630370

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.