TY - GEN
T1 - Sentiment Analysis for German Facebook Pages
AU - Steinbauer, Florian
AU - Kröll, Mark
PY - 2016
Y1 - 2016
N2 - Social media monitoring has become an important means for business analytics and trend detection, for instance, analyzing the sentiment towards a certain product or decision. While a lot of work has been dedicated to analyze sentiment for English texts, much less effort has been put into providing accurate sentiment classification for the German language. In this paper, we analyze three established classifiers for the German language with respect to Facebook posts. We then present our own hierarchical approach to classify sentiment and evaluate it using a data set of ~640 Facebook posts from corporate as well as governmental Facebook pages. We compare our approach to three sentiment classifiers for German, i.e. AlchemyAPI, Semantria and SentiStrength. With an accuracy of 70 %, our approach performs better than the other classifiers. In an application scenario, we demonstrate our classifierl’s ability to monitor changes in sentiment with respect to the refugee crisis.
AB - Social media monitoring has become an important means for business analytics and trend detection, for instance, analyzing the sentiment towards a certain product or decision. While a lot of work has been dedicated to analyze sentiment for English texts, much less effort has been put into providing accurate sentiment classification for the German language. In this paper, we analyze three established classifiers for the German language with respect to Facebook posts. We then present our own hierarchical approach to classify sentiment and evaluate it using a data set of ~640 Facebook posts from corporate as well as governmental Facebook pages. We compare our approach to three sentiment classifiers for German, i.e. AlchemyAPI, Semantria and SentiStrength. With an accuracy of 70 %, our approach performs better than the other classifiers. In an application scenario, we demonstrate our classifierl’s ability to monitor changes in sentiment with respect to the refugee crisis.
U2 - 10.1007/978-3-319-41754-7_44
DO - 10.1007/978-3-319-41754-7_44
M3 - Conference paper
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 427
EP - 432
BT - International Conference on Applications of Natural Language to Information Systems
T2 - 21st International Conference on Applications of Natural Language to Information Systems
Y2 - 22 June 2016 through 24 June 2016
ER -
