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            <title>Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)</title>
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    <abstract>Expressing opinions and emotions on social media becomes a frequent activity in daily life. People express their opinions about various targets via social media and they are also interested to know about other opinions on the same target. Automatically identifying the sentiment of these texts and also the strength of the opinions is an enormous help for people and organizations who are willing to use this information for their goals. In this paper, we present a rule-based approach for German sentiment analysis. The proposed model provides a fine-grained annotation for German texts, which represents the sentiment strength of the input text using two scores: positive and negative. The scores show that if the text contains any positive or negative opinion as well as the strength of each positive and negative opinions. To this aim, a German opinion dictionary of 1,864 words is prepared and compared with other opinion dictionaries for German. We also introduce a new dataset for German sentiment analysis. The dataset contains 500 short texts from social media about German celebrities and is annotated by three annotators. The results show that the proposed unsupervised model outperforms the supervised machine learning techniques. Moreover, the new dictionary performs better than other German opinion dictionaries.</abstract>
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%0 Conference Proceedings
%T Fine-grained German Sentiment Analysis on Social Media
%A Momtazi, Saeedeh
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F momtazi-2012-fine
%X Expressing opinions and emotions on social media becomes a frequent activity in daily life. People express their opinions about various targets via social media and they are also interested to know about other opinions on the same target. Automatically identifying the sentiment of these texts and also the strength of the opinions is an enormous help for people and organizations who are willing to use this information for their goals. In this paper, we present a rule-based approach for German sentiment analysis. The proposed model provides a fine-grained annotation for German texts, which represents the sentiment strength of the input text using two scores: positive and negative. The scores show that if the text contains any positive or negative opinion as well as the strength of each positive and negative opinions. To this aim, a German opinion dictionary of 1,864 words is prepared and compared with other opinion dictionaries for German. We also introduce a new dataset for German sentiment analysis. The dataset contains 500 short texts from social media about German celebrities and is annotated by three annotators. The results show that the proposed unsupervised model outperforms the supervised machine learning techniques. Moreover, the new dictionary performs better than other German opinion dictionaries.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/999_Paper.pdf
%P 1215-1220

Markdown (Informal)

[Fine-grained German Sentiment Analysis on Social Media](http://www.lrec-conf.org/proceedings/lrec2012/pdf/999_Paper.pdf) (Momtazi, LREC 2012)

• Fine-grained German Sentiment Analysis on Social Media (Momtazi, LREC 2012)

ACL

• Saeedeh Momtazi. 2012. Fine-grained German Sentiment Analysis on Social Media. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 1215–1220, Istanbul, Turkey. European Language Resources Association (ELRA).