Name
Abstract | ||
|---|---|---|
Social networking platforms such as Twitter have become a global phenomenon. Net citizens use such services as part of their daily lives. Given this, the opportunities to mine such data resources is of considerable interest to researchers, businesses and government alike. Many researchers have explored the analysis of such resources to support sentiment analysis and event detection amongst many other scenarios. However the potential to more accurately gauge the sentiment based upon gender is increasingly desirable, e.g. for brand analysis. In this paper we present a Cloud-based solution that supports gender identification through a mixed solution combining name classification, image recognition and gender targeted analysis. This paper outlines the Cloud infrastructure and the use of the systems for a range of gender-oriented sentiment classification scenarios. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/HPCC-SmartCity-DSS.2016.99 | PROCEEDINGS OF 2016 IEEE 18TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 14TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 2ND INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS) |
Keywords | Field | DocType |
Twitter, sentiment analysis, gender identification, machine learning, data visualisation | Data science,Social media analytics,Data visualization,Social network,Sentiment analysis,Data resources,Computer science,Phenomenon,Government,Cloud computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Wang | 1 | 50 | 10.52 |
| Mingyu Gao | 2 | 0 | 0.34 |
| Richard O. Sinnott | 3 | 3 | 2.08 |