Name: HENRIQUE GOMES BASONI
Publication date: 14/04/2015
Advisor:
Name |
Role |
|---|---|
| ELIAS SILVA DE OLIVEIRA | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| ELIAS SILVA DE OLIVEIRA | Advisor * |
| PATRICK MARQUES CIARELLI | External Examiner * |
Summary: Since its inception, virtual social networks like Twitter have reached exorbitant amount of users worldwide, making it an immeasurable potential environment for social research, economic, cultural and etc. Increasingly researchers have turned their attention to the great mass of data generated daily in this environment. However, handling large amounts of data is a costly task when performed manually. The objective of this research is to propose a set of tools and methodology that it can reduce the human effort spent in the organization
of large masses of data from social networks. To achieve this goal, we propose an iterative work model that makes the most of existing knowledge in a small amount of data manually analyzed by experts. The working model combines information retrieval techniques such as classification and clustering algorithms in order to make the result of the most similar process to what the expert would get if carried out completely manually. The proposed model was put to the test with use of two sets of extracted data from Twitter and manually classified before this research. The results were promising.
