Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918150053822464 |
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| author | Bilehsavar, Meysam Shirdel Mahmoudi, Negin Torkamani, Mohammad Jalili Kiashemshaki, Kiana |
| author_facet | Bilehsavar, Meysam Shirdel Mahmoudi, Negin Torkamani, Mohammad Jalili Kiashemshaki, Kiana |
| contents | Sentiment analysis is a very important natural language processing activity in which one identifies the polarity of a text, whether it conveys positive, negative, or neutral sentiment. Along with the growth of social media and the Internet, the significance of sentiment analysis has grown across numerous industries such as marketing, politics, and customer service. Sentiment analysis is flawed, however, when applied to foreign languages, particularly when there is no labelled data to train models upon. In this study, we present a transformer ensemble model and a large language model (LLM) that employs sentiment analysis of other languages. We used multi languages dataset. Sentiment was then assessed for sentences using an ensemble of pre-trained sentiment analysis models: bert-base-multilingual-uncased-sentiment, and XLM-R. Our experimental results indicated that sentiment analysis performance was more than 86% using the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets Bilehsavar, Meysam Shirdel Mahmoudi, Negin Torkamani, Mohammad Jalili Kiashemshaki, Kiana Computation and Language I.2.7 Sentiment analysis is a very important natural language processing activity in which one identifies the polarity of a text, whether it conveys positive, negative, or neutral sentiment. Along with the growth of social media and the Internet, the significance of sentiment analysis has grown across numerous industries such as marketing, politics, and customer service. Sentiment analysis is flawed, however, when applied to foreign languages, particularly when there is no labelled data to train models upon. In this study, we present a transformer ensemble model and a large language model (LLM) that employs sentiment analysis of other languages. We used multi languages dataset. Sentiment was then assessed for sentences using an ensemble of pre-trained sentiment analysis models: bert-base-multilingual-uncased-sentiment, and XLM-R. Our experimental results indicated that sentiment analysis performance was more than 86% using the proposed method. |
| title | Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets |
| topic | Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2509.24080 |