# SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment

arXiv: https://arxiv.org/abs/1806.05521 · HTML: https://mlsanity.com/paper/1806.05521/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 26 of 26)

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- [Labor Space: A Unifying Representation of the Labor Market via Large Language Models](https://mlsanity.com/paper/2311.06310/) (2023)
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- [The Language of Weather: Social Media Reactions to Weather Accounting for Climatic and Linguistic Baselines](https://mlsanity.com/paper/2407.07683/) (2024)
- [Capturing Differences in Character Representations Between Communities: An Initial Study with Fandom](https://mlsanity.com/paper/2409.11170/) (2024)
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- [Read the Room Before Your Model Generates](https://mlsanity.com/paper/2610.18820/) (2026)

## Abstract (arXiv)

Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge. Here, we propose SEMAXIS, a simple yet powerful framework to characterize word semantics using many semantic axes in word- vector spaces beyond sentiment. We demonstrate that SEMAXIS can capture nuanced semantic representations in multiple online communities. We also show that, when the sentiment axis is examined, SEMAXIS outperforms the state-of-the-art approaches in building domain-specific sentiment lexicons.