SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
arXiv:1806.05521
Abstract
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.
arXiv commentAccepted in ACL 2018 as a full paper
References in corpus (4)
- Efficient Estimation of Word Representations in Vector Space
- Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter
- ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
- Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus