# Making sense of electrical vehicle discussions using sentiment analysis on closely related news and user comments

arXiv: https://arxiv.org/abs/2112.12327 · HTML: https://mlsanity.com/paper/2112.12327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 23 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment](https://mlsanity.com/paper/1806.05521/) (2018)
- [An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance](https://mlsanity.com/paper/2103.08311/) (2021)

## Abstract (arXiv)

We used a token-wise and document-wise sentiment analysis using both unsupervised and supervised models applied to both news and user reviews dataset. And our token-wise sentiment analysis found a statistically significant difference in sentiment between the two groups (both of which were very large N), our document-wise supervised sentiment analysis found no significant difference in sentiment.