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Sentiment Detection with Character n-Grams
| Citations: | 1 - 0 self |
BibTeX
@MISC{Hartmann_sentimentdetection,
author = {Tino Hartmann and Sebastian Klenk and Andre Burkovski and Gunther Heidemann},
title = {Sentiment Detection with Character n-Grams},
year = {}
}
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Abstract
Abstract—Automatic detection of the sentiment of a given text is a difficult but highly relevant task. Application areas range from financial news, where information about sentiments can be used to predict stock movements, to social media, where user recommendations can determine success or failure of a product. We have developed a methodology, based on character n-grams, to detect sentiments encoded in text. In the course of this paper we will present the founding idea and the algorithms as well as a usage scenario with an evaluation. We discuss the the obtained results in detail and a compare them with those of other popular sentiment detection methodologies. I.
