Name
Abstract | ||
|---|---|---|
A key analytical task across many domains is model building and exploration for predictive analysis. Data is collected, parsed and analyzed for relationships, and features are selected and mapped to estimate the response of a system under exploration. As social media data has grown more abundant, data can be captured that may potentially represent behavioral patterns in society. In turn, this unstructured social media data can be parsed and integrated as a key factor for predictive intelligence. In this paper, we present a framework for the development of predictive models utilizing social media data. We combine feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. In order to explore how predictions might be performed in such a framework, we present results from a user study focusing on social media data as a predictor for movie box-office success. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/VAST.2014.7042495 | IEEE VAST |
Keywords | Field | DocType |
model building,interactive visualizations,movie box-office success,feature selection,data analysis,cinematography,model cross-validation,predictive analytics,data visualisation,predictive intelligence,feature selection mechanisms,similarity comparisons,social networking (online),social media,behavioral patterns,unstructured social media data | Data science,Data mining,Behavioral pattern,Social media,Feature selection,Computer science,Predictive analytics,Model building,Parsing,Analytics | Conference |
ISSN | Citations | PageRank |
2325-9442 | 15 | 0.51 |
References | Authors | |
21 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yafeng Lu | 1 | 160 | 8.21 |
| Robert Kruger | 2 | 58 | 4.07 |
| Dennis Thom | 3 | 178 | 10.72 |
| Feng Wang | 4 | 30 | 1.86 |
| Steffen Koch | 5 | 341 | 26.58 |
| Thomas Ertl | 6 | 4417 | 401.52 |
| Ross Maciejewski | 7 | 542 | 36.54 |