# Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets

arXiv: https://arxiv.org/abs/2509.24080 · HTML: https://mlsanity.com/paper/2509.24080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://mlsanity.com/paper/2310.02374/) (2023)
- [Designing Heterogeneous LLM Agents for Financial Sentiment Analysis](https://mlsanity.com/paper/2401.05799/) (2024)
- [A Survey on Cross-Lingual Summarization](https://mlsanity.com/paper/2203.12515/) (2022)
- [UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation](https://mlsanity.com/paper/2405.20612/) (2024)
- [Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis](https://mlsanity.com/paper/2402.11398/) (2024)
- [A review of sentiment analysis research in Arabic language](https://mlsanity.com/paper/2005.12240/) (2020)
- [An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach](https://mlsanity.com/paper/2402.13871/) (2024)
- [Sanitizing Manufacturing Dataset Labels Using Vision-Language Models](https://mlsanity.com/paper/2506.23465/) (2025)
- [AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets](https://mlsanity.com/paper/2103.01679/) (2021)
- [Simulating a Bias Mitigation Scenario in Large Language Models](https://mlsanity.com/paper/2509.14438/) (2025)
- [Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and Prompt Engineering](https://mlsanity.com/paper/2310.13226/) (2023)

## Built on by (most cited first, 1 of 1)

- [Boosting Accuracy and Interpretability in Multilingual Hate Speech Detection Through Layer Freezing and Explainable AI](https://mlsanity.com/paper/2601.02697/) (2026)

## Abstract (arXiv)

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.