A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
Abstrak
There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff’s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.
Topik & Kata Kunci
Penulis (5)
Bharathi Raja Chakravarthi
Navya Jose
Shardul Suryawanshi
E. Sherly
John P. McCrae
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 237×
- Sumber Database
- Semantic Scholar
- DOI
- 10.5281/ZENODO.4015234
- Akses
- Open Access ✓