Abstract
This paper presents multidimensional Social Opinion Mining on user-generated content gathered from newswires and social networking services in three different languages: English —a high-resourced language, Maltese —a low-resourced language, and Maltese-English —a code-switched language. Multiple fine-tuned neural classification language models which cater for the i) English, Maltese and Maltese-English languages as well as ii) five different social opinion dimensions, namely subjectivity, sentiment polarity, emotion, irony and sarcasm, are presented. Results per classification model for each social opinion dimension are discussed.- Anthology ID:
- 2021.ranlp-1.36
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 309–314
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.36
- DOI:
- Cite (ACL):
- Keith Cortis, Kanishk Verma, and Brian Davis. 2021. Fine-tuning Neural Language Models for Multidimensional Opinion Mining of English-Maltese Social Data. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 309–314, Held Online. INCOMA Ltd..
- Cite (Informal):
- Fine-tuning Neural Language Models for Multidimensional Opinion Mining of English-Maltese Social Data (Cortis et al., RANLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.ranlp-1.36.pdf