Beyond Classification: A Multilingual mBERT Framework for Sentiment Analysis, Zero-Shot Categorization, and Severity Assessment on social media

Sunny Suresh Nahar and Prashant Prakashrao Agnihotri

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 4, 17-25

Abstract: Considering the growth of multilingual user made content within social-media platforms, there is an urgent need for developing scalable, language-agnostic approaches for their analysis. Within this paper, we analyze mBERT's performance in sentiment classification in a binary setting as well as the possibility of performing transfer learning between languages. Specifically, the fine-tuned model is applied for sentiment analysis of tweets from the preprocessed TweetEval dataset, obtaining 79.2% of accuracy and 74.7% of the F1 score. It is shown that cross-language transfer learning without any preliminary training on multilingual sentiment datasets provides quite satisfactory performance. However, a more complex approach can be used, which consists of applying filtering of negative sentiments, categorization of subcategories through a sentence transformer with zero-shot settings, and grouping the resulting data in several major categories to obtain severity scores according to frequency thresholds. The application of the sentiment classification with transformers in combination with issue prioritization makes it possible to develop an end-to-end approach to structuring multilingual social media content.

Keywords: Cross-lingual Transfer; mBERT; Sentiment Analysis; Severity Assessment; Social Media Analytics (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST261341 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST261341/IJSRST261341 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i4:id:1719

DOI: 10.32628/IJSRST261341

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().