Cross-Media Sentiment Classification and Application to Box-Office Forecasting

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.
Document type :
Conference papers
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01203008
Contributor : Lip6 Publications <>
Submitted on : Tuesday, September 22, 2015 - 10:35:18 AM
Last modification on : Thursday, March 21, 2019 - 1:10:05 PM

Identifiers

  • HAL Id : hal-01203008, version 1

Citation

Elie Guardia-Sebaoun, Abdelhalim Rafrafi, Vincent Guigue, Patrick Gallinari. Cross-Media Sentiment Classification and Application to Box-Office Forecasting. the 10th Conference on Open Research Areas in Information Retrieval (OAIR '13), May 2013, Lisbon, Portugal. pp.201-208. ⟨hal-01203008⟩

Share

Metrics

Record views

84