<p>In the era of global digital communication, understanding user sentiment across multiple languages is a critical challenge with wide-ranging applications in opinion mining, customer feedback analysis, and social media monitoring. This study advances the field of language-independent sentiment analysis by leveraging prompt-based fine-tuning with state-of-the-art transformer models. The performance of classical machine learning approaches, hybrid deep learning architectures, and multilingual transformer models is evaluated across eight typologically diverse languages: Arabic, English, French, German, Hindi, Italian, Portuguese, and Spanish. Baseline models are established using traditional machine learning approaches such as Support Vector Machines (SVM) and Logistic Regression, with feature extraction methods like TF-IDF. A hybrid deep learning model is introduced, combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) to capture local and sequential text patterns. Building on these, pre-trained multilingual transformer models, specifically BERT-base-multilingual and XLM-RoBERTa, are fine-tuned for language-independent sentiment classification tasks. The key contribution lies in the implementation of prompt-based fine-tuning strategies for language independent sentiment analysis. Using (1) prefix prompts and (2) cloze-style prompts, a unified framework is established that employs templates designed in one language and evaluates their performance on data from the remaining <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_3559_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="53" /> </InlineMediaObject> <EquationSource Format="TEX">$(n-1)$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation> languages. Experimental results demonstrate that transformer models, particularly XLM-RoBERTa with prompt-based fine-tuning outperform both classical and deep learning methods. With only 32 training examples per class, prefix prompts produce results comparable to standard fine-tuning, which typically uses 70-80% of the data for training. This highlights the potential of prompt-based learning for scalable, multilingual sentiment analysis in diverse language settings.</p>

Prompt-based fine-tuning with multilingual transformers for language-independent sentiment analysis

期刊论文
出版模式：

开放获取
发表日期：

2025年7月1日

查看全文

Faizad Ullah,
Safiullah Faizullah,
Imdad Ullah Khan,
Turki Alghamdi,
Toqeer Ali Syed,
Ahmad B. Alkhodre,
Muhammad Sohaib Ayub,
Asim Karim

摘要

In the era of global digital communication, understanding user sentiment across multiple languages is a critical challenge with wide-ranging applications in opinion mining, customer feedback analysis, and social media monitoring. This study advances the field of language-independent sentiment analysis by leveraging prompt-based fine-tuning with state-of-the-art transformer models. The performance of classical machine learning approaches, hybrid deep learning architectures, and multilingual transformer models is evaluated across eight typologically diverse languages: Arabic, English, French, German, Hindi, Italian, Portuguese, and Spanish. Baseline models are established using traditional machine learning approaches such as Support Vector Machines (SVM) and Logistic Regression, with feature extraction methods like TF-IDF. A hybrid deep learning model is introduced, combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) to capture local and sequential text patterns. Building on these, pre-trained multilingual transformer models, specifically BERT-base-multilingual and XLM-RoBERTa, are fine-tuned for language-independent sentiment classification tasks. The key contribution lies in the implementation of prompt-based fine-tuning strategies for language independent sentiment analysis. Using (1) prefix prompts and (2) cloze-style prompts, a unified framework is established that employs templates designed in one language and evaluates their performance on data from the remaining $(n-1)$ $(n - 1)$ languages. Experimental results demonstrate that transformer models, particularly XLM-RoBERTa with prompt-based fine-tuning outperform both classical and deep learning methods. With only 32 training examples per class, prefix prompts produce results comparable to standard fine-tuning, which typically uses 70-80% of the data for training. This highlights the potential of prompt-based learning for scalable, multilingual sentiment analysis in diverse language settings.