SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
This work addresses the problem of domain-specific semantic variation for natural language processing applications, offering a novel method that is incremental in its approach.
The authors tackled the challenge of capturing domain-specific word semantics across communities by proposing SemAxis, a lightweight framework that uses semantic axes in word-vector spaces. They demonstrated that SemAxis outperforms state-of-the-art methods in building domain-specific sentiment lexicons, achieving improved performance in sentiment analysis tasks.
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.