Name
Abstract | ||
|---|---|---|
This article aims at demonstrating the interest of opinion mining on Twitter data for the box-office prediction. Whilst most approaches in box-office forecasting focus on expert knowledge (actor celebrity, film budget...), or more recently on Twitter volumetric features, we want to show that the tweet's content is also important to make an efficient decision. Firstly we focus on the cross-media sentiment classification task, by studying the impact different algorithms and data sources have on the accuracy of sentiment classification on Twitter. Secondly, models allow us to to build high level sentiment features for the box-office forecasting problem. We demonstrate the interest of opinion mining derived features for this second task.
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Year | Venue | Keywords |
|---|---|---|
2013 | OAIR | twitter data,high level sentiment feature,data source,cross-media sentiment classification task,box-office forecasting problem,box-office prediction,box-office forecasting focus,opinion mining,sentiment classification,cross-media sentiment classification,twitter volumetric feature |
Field | DocType | ISBN |
Data science,Sentiment analysis,Support vector machine,Cross media,Engineering | Conference | 978-2-905450-09-8 |
Citations | PageRank | References |
2 | 0.37 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Élie Guàrdia-Sebaoun | 1 | 9 | 1.20 |
| Abdelhalim Rafrafi | 2 | 6 | 1.76 |
| Vincent Guigue | 3 | 157 | 17.41 |
| Patrick Gallinari | 4 | 1856 | 187.19 |