ArticleScientific reports2024
A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Automating data extraction in meta-research: A multi-model benchmark in network psychometrics papers.Behavior research methods · 2026Article
- LLM-enabled adaptive scheduling in IoT sensing for optimized network performance.Scientific reports · 2026Article
- A unified GRU model for cryptocurrency price prediction and harsh price movement detection using enhanced sentiment analysis.Scientific reports · 2026Article
- Cross-language hotel review sentiment analysis via multi-agent federated learning with heterogeneous graph attention networks.Scientific reports · 2026Article
- Multimodal data integration and machine learning methods for early detection and risk prediction of pulmonary diseases in athletes.Frontiers in medicine · 2026Article
- Do language families matter? Evaluating LLMs for sentiment analysis through a hierarchical cross-lingual lens.Frontiers in artificial intelligence · 2026Article
- CLAS-Net: A study on cross-lingual intelligent sentiment analysis model fusing semantic alignment.PloS one · 2026Article
- Automatic speech recognition for Telugu: a comparative analysis of Wav2Vec 2.0 model variants and hyperparameter tuning.Frontiers in artificial intelligence · 2026Article
- Prompt-based fine-tuning with multilingual transformers for language-independent sentiment analysis.Scientific reports · 2025Article
- The usage of a transformer based and artificial intelligence driven multidimensional feedback system in english writing instruction.Scientific reports · 2025Article
- The future of electronic health record-based research: Leveraging informatics in genetic counseling research.Journal of genetic counseling · 2025Article
- Sentiment analysis of social media for enhancing disaster response strategies.Frontiers in public health · 2025Article
- Sentiment analysis of research attention: the Altmetric proof of concept.Frontiers in research metrics and analytics · 2025Article
- Evolving techniques in sentiment analysis: a comprehensive review.PeerJ. Computer science · 2025Article
- Evaluating sentiment analysis models in healthcare: addressing bias and enhancing interpretability.Frontiers in public health · 2025Article
- An assessment of ChatGPT in error detection for thyroid ultrasound reports: A comparative study with ultrasound physicians.Digital healthArticle
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Authors and funding
6 authors.
Funding
Abstract
Sentiment analysis is an essential task in natural language processing that involves identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.