# Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon

arXiv: https://arxiv.org/abs/2004.14357 · HTML: https://mlsanity.com/paper/2004.14357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding](https://mlsanity.com/paper/2305.14592/) (2023)

## Abstract (arXiv)

Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment classification. One major issue with this approach is that many sentiment words are domain dependent. That is, they may be positive in some domains but negative in some others. We refer to this problem as domain polarity-changes of words. Detecting such words and correcting their sentiment for an application domain is very important. In this paper, we propose a graph-based technique to tackle this problem. Experimental results show its effectiveness on multiple real-world datasets.