Detecting Entity-Related Events and Sentiments from Tweets Using Multilingual Resources
This article presents the details of the participation of the OPTAH
team to the CLEF 2012 RepLab profiling (polarity classification) and monitoring
tasks. Specifically, we present the manner in which the OPAL system has been
modified to deal with opinions in tweets and how the use of rules involving the
language use in social-media can help to achieve good results as far as polarity
classification is concerned, even in a language for which we have just a small
polarity lexicon. Additionally, we show how we can employ the values computed
for sentiment intensity (especially the negative ones) to classify the importance
of event-related clusters of tweets. Our methods, although quite simple, obtained
promising results in the RepLab evaluations.
BALAHUR DOBRESCU Alexandra;
TANEV Hristo;
2013-01-09
Fundazione Bruno Kessler (FBK) Press
JRC73634
2038-4963,
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