Business intelligence from social media: A study from the VAST box office challenge

IEEE Computer Graphics and Applications

Volumn 34, Issue 5, 2014, Pages 58-69

  Lu, Yafeng ^a   Wang, Feng ^a   Maciejewski, Ross ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Bitly; Computer graphics; Graphics; Linear regression; Movie revenue; Movie reviews; Prediction; Social media; Twitter; VAST Box Office Challenge; Visual analytics; Visualization

Indexed keywords

FLOW VISUALIZATION; FORECASTING; LINEAR REGRESSION;

BITLY; BOX OFFICE; GRAPHICS; MOVIE REVIEWS; SOCIAL MEDIA; TWITTER; VISUAL ANALYTICS;

COMPUTER GRAPHICS;

COMMERCIAL PHENOMENA; HUMAN; INFORMATION SCIENCE; LEISURE; PROCEDURES; SOCIAL MEDIA; STATISTICS AND NUMERICAL DATA; THEORETICAL MODEL;

COMMERCE; HUMANS; INFORMATICS; LEISURE ACTIVITIES; MODELS, THEORETICAL; SOCIAL MEDIA;

EID: 84907468339     PISSN: 02721716     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCG.2014.61     Document Type: Article

References (12)

1. T. Schreck and D. Keim, "Visual Analysis of Social Media Data," Computer, vol. 46, no. 5, 2013, pp. 68-75.
2. H. Bosch et al., "Scatterblogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering," IEEE Trans. Visualization and Computer Graphics, vol. 19, no. 12, 2013, pp. 2022-2031.
3. J. Chae et al., "Spatiotemporal Social Media Analytics for Abnormal Event Detection and Examination Using Seasonal-Trend Decomposition," Proc. 2012 IEEE Conf. Visual Analytics Science and Technology (VAST 12), 2012, pp. 143-152.
4. X. Wang et al., "I-SI: Scalable Architecture for Analyzing Latent Topical Level Information from Social Media Data," Computer Graphics Forum, vol. 31, no. 3, part 4, 2012, pp. 1275-1284.
5. M.C. Hao et al., "Visual Sentiment Analysis of Customer Feedback Streams Using Geo-temporal Term Associations," Information Visualization, vol. 12, nos. 3-4, 2013, pp. 273-290.
6. S. Baccianella, A. Esuli, and F. Sebastiani, "SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining," Proc. Int'l Conf. Language Resources and Evaluation, 2010, pp. 2200-2204.
7. R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2008.
8. R. Panaligan and A. Chen, Quantifying Movie Magic with Google Search, white paper, Google, 2013.
9. S. Asur and B.A. Huberman, "Predicting the Future with Social Media," Proc. IEEE/WIC/ACM Int'l Conf. Web Intelligence and Intelligent Agent Technology, 2010, pp. 492-499.
10. G. Rowe and G. Wright, "The Delphi Technique as a Forecasting Tool: Issues and Analysis," Int'l J. Forecasting, vol. 15, no. 4, 1999, pp. 353-375.
11. W. Zhang and S. Skiena, "Improving Movie Gross Prediction through News Analysis," Proc. IEEE/WIC/ACM Int'l Joint Conf. Web Intelligence and Intelligent Agent Technology, 2009, pp. 301-304.
12. M. Joshi et al., "Movie Reviews and Revenues: An Experiment in Text Regression," Human Language Technologies: The 2010 Ann. Conf. North Am. Chapter of the Assoc. for Computational Linguistics, 2010, pp. 293-296.