@inproceedings{caruana-etal-2026-sentimalti,
title = "{S}enti{M}alti: A {M}altese Sentiment Analysis Dataset and Models",
author = "Caruana, Ian and
Vella, Matthew and
Zammit, Fabio and
Micallef, Kurt and
Borg, Claudia",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.630/",
doi = "10.63317/4kw8df57bza3",
pages = "7927--7936",
abstract = "We present SentiMalti, a new Maltese social media sentiment resource and accompanying baselines. We scrape user-generated content from YouTube, Reddit, and Facebook, then apply a Maltese-aware preprocessing pipeline (cleaning, personally identifiable information anonymisation, sentence splitting, and sentence-level language filtering) to retain Maltese sentences while tolerating realistic code-switching. The resulting crowdsourced dataset contains 2,327 sentences annotated for positive (39{\%}), negative (31{\%}), and neutral (30{\%}) sentiment. We integrate prior Maltese datasets to create a combined benchmark of 3,772 instances. We evaluate fine-tuned encoder models (BERTu, Glot500) and few-shot prompting with instruction-tuned multilingual LLMs (Aya-101, Gemma 2 Instruct 9B). On the full test set, five-shot Aya-101 attains 68.65 macro-F1, closely followed by a fine-tuned BERTu at 68.36 macro-F1. Error analysis reveals complementary strengths: BERTu better separates polarised classes, while Aya-101 tends to over-predict the neutral class. We release the dataset splits, code, and a fine-tuned BERTu model to facilitate further work in Maltese NLP and sentiment analysis."
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<abstract>We present SentiMalti, a new Maltese social media sentiment resource and accompanying baselines. We scrape user-generated content from YouTube, Reddit, and Facebook, then apply a Maltese-aware preprocessing pipeline (cleaning, personally identifiable information anonymisation, sentence splitting, and sentence-level language filtering) to retain Maltese sentences while tolerating realistic code-switching. The resulting crowdsourced dataset contains 2,327 sentences annotated for positive (39%), negative (31%), and neutral (30%) sentiment. We integrate prior Maltese datasets to create a combined benchmark of 3,772 instances. We evaluate fine-tuned encoder models (BERTu, Glot500) and few-shot prompting with instruction-tuned multilingual LLMs (Aya-101, Gemma 2 Instruct 9B). On the full test set, five-shot Aya-101 attains 68.65 macro-F1, closely followed by a fine-tuned BERTu at 68.36 macro-F1. Error analysis reveals complementary strengths: BERTu better separates polarised classes, while Aya-101 tends to over-predict the neutral class. We release the dataset splits, code, and a fine-tuned BERTu model to facilitate further work in Maltese NLP and sentiment analysis.</abstract>
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%0 Conference Proceedings
%T SentiMalti: A Maltese Sentiment Analysis Dataset and Models
%A Caruana, Ian
%A Vella, Matthew
%A Zammit, Fabio
%A Micallef, Kurt
%A Borg, Claudia
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F caruana-etal-2026-sentimalti
%X We present SentiMalti, a new Maltese social media sentiment resource and accompanying baselines. We scrape user-generated content from YouTube, Reddit, and Facebook, then apply a Maltese-aware preprocessing pipeline (cleaning, personally identifiable information anonymisation, sentence splitting, and sentence-level language filtering) to retain Maltese sentences while tolerating realistic code-switching. The resulting crowdsourced dataset contains 2,327 sentences annotated for positive (39%), negative (31%), and neutral (30%) sentiment. We integrate prior Maltese datasets to create a combined benchmark of 3,772 instances. We evaluate fine-tuned encoder models (BERTu, Glot500) and few-shot prompting with instruction-tuned multilingual LLMs (Aya-101, Gemma 2 Instruct 9B). On the full test set, five-shot Aya-101 attains 68.65 macro-F1, closely followed by a fine-tuned BERTu at 68.36 macro-F1. Error analysis reveals complementary strengths: BERTu better separates polarised classes, while Aya-101 tends to over-predict the neutral class. We release the dataset splits, code, and a fine-tuned BERTu model to facilitate further work in Maltese NLP and sentiment analysis.
%R 10.63317/4kw8df57bza3
%U https://aclanthology.org/2026.lrec-1.630/
%U https://doi.org/10.63317/4kw8df57bza3
%P 7927-7936
Markdown (Informal)
[SentiMalti: A Maltese Sentiment Analysis Dataset and Models](https://aclanthology.org/2026.lrec-1.630/) (Caruana et al., LREC 2026)
ACL
- Ian Caruana, Matthew Vella, Fabio Zammit, Kurt Micallef, and Claudia Borg. 2026. SentiMalti: A Maltese Sentiment Analysis Dataset and Models. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7927–7936, Palma de Mallorca, Spain. ELRA Language Resource Association.