Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/25677| Author(s): | Rosa, H. Batista, F. Carvalho, J. |
| Date: | 2014 |
| Title: | Twitter topic fuzzy fingerprints |
| ISSN: | 1098-7584 |
| DOI (Digital Object Identifier): | 10.1109/FUZZ-IEEE.2014.6891781 |
| Abstract: | In this paper we propose to approach the subject of Twitter Topic Detection using a new technique called Topic Fuzzy Fingerprints. A comparison is made with two popular text classification techniques, Support Vector Machines (SVM) and k-Nearest Neighbours (kNN). Preliminary results show that Twitter Topic Fuzzy Fingerprints outperforms the other two techniques achieving better Precision and Recall, while still being much faster, which is an essential feature when processing large volumes of streaming data. |
| Peerreviewed: | yes |
| Access type: | Open Access |
| Appears in Collections: | IT-CRI - Comunicações a conferências internacionais |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| conferenceobject_18038.pdf | Versão Aceite | 292,72 kB | Adobe PDF | View/Open |
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