# Sentiment Analysis of Covid-related Reddits

arXiv: https://arxiv.org/abs/2205.06863 · HTML: https://mlsanity.com/paper/2205.06863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 13 May 2022.

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

- [Corpus Statistics in Text Classification of Online Data](https://mlsanity.com/paper/1803.06390/) (2018)
- [Sentiment Analysis of the COVID-related r/Depression Posts](https://mlsanity.com/paper/2108.06215/) (2021)

## Abstract (arXiv)

This paper focuses on Sentiment Analysis of Covid-19 related messages from the r/Canada and r/Unitedkingdom subreddits of Reddit. We apply manual annotation and three Machine Learning algorithms to analyze sentiments conveyed in those messages. We use VADER and TextBlob to label messages for Machine Learning experiments. Our results show that removal of shortest and longest messages improves VADER and TextBlob agreement on positive sentiments and F-score of sentiment classification by all the three algorithms