Name
Abstract | ||
|---|---|---|
We aim to classify people's voting intentions by the content of their Tweets about the Scottish Independence Referendum (hereafter, IndyRef). By observing the IndyRef dataset, we find that people not only discussed the vote, but raised topics related to an independent Scotland including oil reserves, currency, nuclear weapons, and national debt. We show that the views communicated on these topics can inform us of the individuals' voting intentions (\"Yes\" vs. \"No\"). In particular, we argue that an accurate classifier can be designed by leveraging the differences in the features' usage across different topics related to voting intentions. We demonstrate improvements upon a Naive Bayesian classifier using the topics enrichment method. Our new classifier identifies the closest topic for each unseen tweet, based on those topics identified in the training data. Our experiments show that our proposed Topics-Based Naive Bayesian classifier improves accuracy by 7.8% over the classical Naive Bayesian baseline. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.14236/ewic/FDIA2015.10 | SIGIR |
Field | DocType | Citations |
Data science,Biology and political orientation,Voting,Information retrieval,Feature selection,Naive Bayes classifier,Computer science,Topic model,Classifier (linguistics),Independence referendum,Bayes' theorem | Conference | 11 |
PageRank | References | Authors |
0.58 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anjie Fang | 1 | 35 | 5.93 |
| Iadh Ounis | 2 | 3438 | 234.59 |
| Philip Habel | 3 | 34 | 2.88 |
| Craig Macdonald | 4 | 2588 | 178.50 |
| Nut Limsopatham | 5 | 172 | 14.86 |