# Developing a concept-level knowledge base for sentiment analysis in Singlish

arXiv: https://arxiv.org/abs/1707.04408 · HTML: https://mlsanity.com/paper/1707.04408/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [On Language Models for Creoles](https://mlsanity.com/paper/2109.06074/) (2021)
- [LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content](https://mlsanity.com/paper/2407.10995/) (2024)
- [Augmenting semantic lexicons using word embeddings and transfer learning](https://mlsanity.com/paper/2109.09010/) (2021)
- [Stylistic Evolution and LLM Neutrality in Singlish Language](https://mlsanity.com/paper/2601.06580/) (2026)

## Abstract (arXiv)

In this paper, we present Singlish sentiment lexicon, a concept-level knowledge base for sentiment analysis that associates multiword expressions to a set of emotion labels and a polarity value. Unlike many other sentiment analysis resources, this lexicon is not built by manually labeling pieces of knowledge coming from general NLP resources such as WordNet or DBPedia. Instead, it is automatically constructed by applying graph-mining and multi-dimensional scaling techniques on the affective common-sense knowledge collected from three different sources. This knowledge is represented redundantly at three levels: semantic network, matrix, and vector space. Subsequently, the concepts are labeled by emotions and polarity through the ensemble application of spreading activation, neural networks and an emotion categorization model.