DMCA
FBM-Yahoo! at RepLab 2012
Cached
Download Links
| Citations: | 3 - 0 self |
BibTeX
@MISC{Chenlo_fbm-yahoo!at,
author = {Jose M. Chenlo and Jordi Atserias and Carlos Rodriguez and Roi Blanco},
title = {FBM-Yahoo! at RepLab 2012},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract. This paper describes FBM-Yahoo!’s participation in the profiling task of RepLab 2012, which aims at determining whether a given tweet is related to a specific company and, in if this being the case, whether it contains a positive or negative statement related to the company’s reputation or not. We addressed both problems (ambiguity and polarity reputation) using Support Vector Machines (SVM) classifiers and lexicon-based techniques, building automatically company profiles and bootstrapping background data. Concretely, for the ambiguity task we employed a linear SVM classifier with a token-based representation of relevant and irrelevant information extracted from the tweets and Freebase resources. With respect to polarity classification, we combined SVM lexicon-based approaches with bootstrapping in order to determine the final polarity label of a tweet. 1
Keyphrases
linear svm classifier support vector machine ambiguity task company profile lexicon-based technique polarity reputation final polarity label freebase resource profiling task svm lexicon-based approach irrelevant information polarity classification negative statement token-based representation background data fbm-yahoo participation specific company
