Name
Abstract | ||
|---|---|---|
Online social media has changed the way of interacting among users, nowadays, is used as a tool for expressing polarized opinions related to a global or specific context. Valuable information can be gathered in real-time basis and can help to determine if such data has a social impact on users represented as comfort or discomfort on a political domain. Analyzing data related to political domains like government, elections, security & defense and health insurance are important for measuring social mood and predicting whether there is a positive or negative tendency on selected populations. This paper presents a mood analysis methodology on Twitter data to predict social sentiment on political events. The proposed methodology is done by gathering streams of Twitter's information, then converted into trained data for processing and classification such that we can statistically predict if there is a positive or negative tendency on political events. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IWBF.2017.7935106 | 2017 5th International Workshop on Biometrics and Forensics (IWBF) |
Keywords | Field | DocType |
Data,Big Data,Nave Bayes,Regularized Regression,Twitter,prediction,forecasting | Mood,Internet privacy,Political science,Social media,Voting,Health insurance,Social impact,Politics,Government | Conference |
ISSN | ISBN | Citations |
2381-6120 | 978-1-5090-5792-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Hernandez-Suarez | 1 | 0 | 0.34 |
| Gabriel Sanchez-Perez | 2 | 28 | 13.00 |
| V. Martinez-Hernandez | 3 | 0 | 0.34 |
| Héctor Pérez Meana | 4 | 20 | 7.49 |
| Karina Toscano-Medina | 5 | 19 | 8.94 |
| Mariko Nakano | 6 | 0 | 0.34 |
| Victor Sanchez | 7 | 144 | 31.22 |