Name
Title | ||
|---|---|---|
Analyzing Disproportionate Reaction via Comparative Multilingual Targeted Sentiment in Twitter. |
Abstract | ||
|---|---|---|
Global events such as terrorist attacks are commented upon in social media, such as Twitter, in different languages and from different parts of the world. Most prior studies have focused on monolingual sentiment analysis, and therefore excluded an extensive proportion of the Twitter userbase. In this paper, we perform a multilingual comparative sentiment analysis study on the terrorist attack in Paris, during November 2015. In particular, we look at targeted sentiment, investigating opinions on specific entities, not simply the general sentiment of each tweet. Given the potentially inflammatory and polarizing effect that these types of tweets may have on attitudes, we examine the sentiments expressed about different targets and explore whether disproportionate reaction was expressed about such targets across different languages. Specifically, we assess whether the sentiment for French speaking Twitter users during the Paris attack differs from English-speaking ones. We identify disproportionately negative attitudes in the English dataset over the French one towards some entities and, via a crowdsourcing experiment, illustrate that this also extends to forming an annotator bias.
|
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3110025.3110066 | ASONAM '17: Advances in Social Networks Analysis and Mining 2017
Sydney
Australia
July, 2017 |
Field | DocType | ISBN |
World Wide Web,Social media,Computer science,Sentiment analysis,Crowdsourcing,Terrorism,Artificial intelligence,Deep learning | Conference | 978-1-4503-4993-2 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karin Sim Smith | 1 | 2 | 0.71 |
| Richard Mccreadie | 2 | 403 | 32.43 |
| Craig Macdonald | 3 | 2588 | 178.50 |
| Iadh Ounis | 4 | 3438 | 234.59 |