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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2405.02887 |
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| _version_ | 1866916378525564928 |
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| author | Kathunia, Aekansh Kaif, Mohammad Arora, Nalin Narotam, N |
| author_facet | Kathunia, Aekansh Kaif, Mohammad Arora, Nalin Narotam, N |
| contents | People communicate in more than 7,000 languages around the world, with around 780 languages spoken in India alone. Despite this linguistic diversity, research on Sentiment Analysis has predominantly focused on English text data, resulting in a disproportionate availability of sentiment resources for English. This paper examines the performance of transformer models in Sentiment Analysis tasks across multilingual datasets and text that has undergone machine translation. By comparing the effectiveness of these models in different linguistic contexts, we gain insights into their performance variations and potential implications for sentiment analysis across diverse languages. We also discuss the shortcomings and potential for future work towards the end. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02887 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to English Kathunia, Aekansh Kaif, Mohammad Arora, Nalin Narotam, N Computation and Language Artificial Intelligence People communicate in more than 7,000 languages around the world, with around 780 languages spoken in India alone. Despite this linguistic diversity, research on Sentiment Analysis has predominantly focused on English text data, resulting in a disproportionate availability of sentiment resources for English. This paper examines the performance of transformer models in Sentiment Analysis tasks across multilingual datasets and text that has undergone machine translation. By comparing the effectiveness of these models in different linguistic contexts, we gain insights into their performance variations and potential implications for sentiment analysis across diverse languages. We also discuss the shortcomings and potential for future work towards the end. |
| title | Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to English |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.02887 |