Summary of Longitudinal Sentiment Classification Of Reddit Posts, by Fabian Nwaoha et al.
Longitudinal Sentiment Classification of Reddit Posts
by Fabian Nwaoha, Ziyad Gaffar, Ho Joon Chun, Marina Sokolova
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a longitudinal study on sentiment classification of Reddit posts written by students from four major Canadian universities over the years 2020-2023. The researchers fine-tuned a sentiment threshold to a range of [-0.075,0.075] and successfully developed classifiers that can categorize post sentiments into positive or negative categories. Notably, the study found consistent results across the four university datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how students from different universities in Canada feel about things they write on Reddit. They analyzed posts from 2020 to 2023 and created a system that can tell if someone is feeling happy or sad based on what they wrote. The good news is that their method worked the same way for all four universities! |
Keywords
* Artificial intelligence * Classification
