Abstract
The SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with bag of words feature vectors and fixed rule multi-kernel learning, for sentiment analysis of tweets. Since tweets on the same topic, made at different times, may exhibit different emotions, their properties such as smoothness and periodicity also vary with time. Our experiments show that, compared to single kernel, multiple kernels are effective in learning the simultaneous presence of multiple properties.- Anthology ID:
- S17-2118
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 709–712
- Language:
- URL:
- https://aclanthology.org/S17-2118
- DOI:
- 10.18653/v1/S17-2118
- Cite (ACL):
- Angel Deborah S, S Milton Rajendram, and T T Mirnalinee. 2017. SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 709–712, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier (S et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S17-2118.pdf