# Language-Independent Sentiment Labelling with Distant Supervision: A Case Study for English, Sepedi and Setswana

arXiv: https://arxiv.org/abs/2511.19818 · HTML: https://mlsanity.com/paper/2511.19818/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 97 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 21 of 21)

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- [Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis](https://mlsanity.com/paper/1505.07302/) (2015)
- [Bangla Text Dataset and Exploratory Analysis for Online Harassment Detection](https://mlsanity.com/paper/2102.02478/) (2021)
- [Enhanced Twitter Sentiment Classification Using Contextual Information](https://mlsanity.com/paper/1605.05195/) (2016)
- [Training Cross-Lingual embeddings for Setswana and Sepedi](https://mlsanity.com/paper/2111.06230/) (2021)

## Abstract (arXiv)

Sentiment analysis is a helpful task to automatically analyse opinions and emotions on various topics in areas such as AI for Social Good, AI in Education or marketing. While many of the sentiment analysis systems are developed for English, many African languages are classified as low-resource languages due to the lack of digital language resources like text labelled with corresponding sentiment classes. One reason for that is that manually labelling text data is time-consuming and expensive. Consequently, automatic and rapid processes are needed to reduce the manual effort as much as possible making the labelling process as efficient as possible. In this paper, we present and analyze an automatic language-independent sentiment labelling method that leverages information from sentiment-bearing emojis and words. Our experiments are conducted with tweets in the languages English, Sepedi and Setswana from SAfriSenti, a multilingual sentiment corpus for South African languages. We show that our sentiment labelling approach is able to label the English tweets with an accuracy of 66%, the Sepedi tweets with 69%, and the Setswana tweets with 63%, so that on average only 34% of the automatically generated labels remain to be corrected.