MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917620004945920 |
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| author | Rani, Priya Negi, Gaurav Fransen, Theodorus McCrae, John P. |
| author_facet | Rani, Priya Negi, Gaurav Fransen, Theodorus McCrae, John P. |
| contents | The present paper introduces new sentiment data, MaCMS, for Magahi-Hindi-English (MHE) code-mixed language, where Magahi is a less-resourced minority language. This dataset is the first Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. Further, we also provide a linguistics analysis of the dataset to understand the structure of code-mixing and a statistical study to understand the language preferences of speakers with different polarities. With these analyses, we also train baseline models to evaluate the dataset's quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04639 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis Rani, Priya Negi, Gaurav Fransen, Theodorus McCrae, John P. Computation and Language The present paper introduces new sentiment data, MaCMS, for Magahi-Hindi-English (MHE) code-mixed language, where Magahi is a less-resourced minority language. This dataset is the first Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. Further, we also provide a linguistics analysis of the dataset to understand the structure of code-mixing and a statistical study to understand the language preferences of speakers with different polarities. With these analyses, we also train baseline models to evaluate the dataset's quality. |
| title | MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.04639 |