Abstract
Unknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their morphological make-up. We do this on German as it has very productive processes of derivation and compounding and many German hapax words, which are likely to bear sentiment, are morphologically complex. We present results of supervised classification experiments on new datasets with morphological parses and polarity annotations.- Anthology ID:
- R17-1081
- Volume:
- Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 625–633
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-049-6_081
- DOI:
- 10.26615/978-954-452-049-6_081
- Cite (ACL):
- Josef Ruppenhofer, Petra Steiner, and Michael Wiegand. 2017. Evaluating the morphological compositionality of polarity. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 625–633, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Evaluating the morphological compositionality of polarity (Ruppenhofer et al., RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_081