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Autor:
Flekova, Lucie; Ferschke, Oliver; Gurevych, Iryna:

Titel:
UKPDIPF
A lexical semantic approach to sentiment polarity prediction in Twitter data

Quelle:
In: Nakov, Preslav;Zesch, Torsten (Hrsg.): Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) Dublin : Association for Computational Linguistics (2014) , 704-710

URL des Volltextes:
http://alt.qcri.org/semeval2014/cdrom/pdf/SemEval2014126.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Ausdruck (Psy), Computerlinguistik, Emotion, Klassifikation, Schriftsprache, Semantik, Soziale Software, Textanalyse


Abstract(original):
We present a sentiment classification system that participated in the SemEval 2014 shared task on sentiment analysis in Twitter. Our system expands tokens in a tweet with semantically similar expressions using a large novel distributional thesaurus and calculates the semantic relatedness of the expanded tweets to word lists repre- senting positive and negative sentiment. This approach helps to assess the polarity of tweets that do not directly contain polarity cues. Moreover, we incorporate syntactic, lexical and surface sentiment features. On the message level, our system achieved the 8th place in terms of macroaveraged F-score among 50 systems, with particularly good performance on the Life-Journal corpus (F1=71.92) and the Twitter sarcasm (F1=54.59) dataset. On the expression level, our system ranked 14 out of 27 systems, based on macro-averaged F-score. (DIPF/Orig.)


DIPF-Abteilung:
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zuletzt verändert: 11.11.2016