---
license: mit
task_categories:
- text-classification
language:
- mt
---

## Sentiment Analysis Data for the Maltese Language

**Dataset Description:**
This dataset contains sentiment analysis data originating from comments on news articles and social media posts. It combines two datasets from Cortis and Davis (2019) and Dingli and Sant (2016).

**Data Structure:**
The data was utilized for the project on [injecting external commonsense knowledge into multilingual Large Language Models](https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs).


**Citation:**
```bibtex
@inproceedings{cortis-davis-2019-social,
    title = "A Social Opinion Gold Standard for the {M}alta Government Budget 2018",
    author = "Cortis, Keith  and
      Davis, Brian",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5547",
    doi = "10.18653/v1/D19-5547",
    pages = "364--369",
    abstract = "We present a gold standard of annotated social opinion for the Malta Government Budget 2018. It consists of over 500 online posts in English and/or the Maltese less-resourced language, gathered from social media platforms, specifically, social networking services and newswires, which have been annotated with information about opinions expressed by the general public and other entities, in terms of sentiment polarity, emotion, sarcasm/irony, and negation. This dataset is a resource for opinion mining based on social data, within the context of politics. It is the first opinion annotated social dataset from Malta, which has very limited language resources available.",
}

@inproceedings{dingli2016sentiment,
  title={Sentiment analysis on Maltese using machine learning},
  author={Dingli, Alexiei and Sant, Nicole},
  booktitle={Proceedings of The Tenth International Conference on Advances in Semantic Processing (SEMAPRO 2016)},
  pages={21--25},
  year={2016}
}

```