Abstract
We investigate the application of kernel methods to representing both structural and lexical knowledge for predicting polarity of opinions in consumer product review. We introduce any-gram kernels which model lexical information in a significantly faster way than the traditional n-gram features, while capturing all possible orders of n-grams n in a sequence without the need to explicitly present a pre-specified set of such orders. We also present an effective format to represent constituency and dependency structure together with aspect terms and sentiment polarity scores. Furthermore, we modify the traditional tree kernel function to compute the similarity based on word embedding vectors instead of exact string match and present experiments using the new models.- Anthology ID:
- W16-4307
- Volume:
- Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Malvina Nissim, Viviana Patti, Barbara Plank
- Venue:
- PEOPLES
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 60–69
- Language:
- URL:
- https://aclanthology.org/W16-4307
- DOI:
- Cite (ACL):
- Rasoul Kaljahi and Jennifer Foster. 2016. Detecting Opinion Polarities using Kernel Methods. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 60–69, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Detecting Opinion Polarities using Kernel Methods (Kaljahi & Foster, PEOPLES 2016)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W16-4307.pdf
- Data
- MPQA Opinion Corpus