# Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment

arXiv: https://arxiv.org/abs/2108.10068 · HTML: https://mlsanity.com/paper/2108.10068/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 69 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Analysis Methods in Neural Language Processing: A Survey](https://mlsanity.com/paper/1812.08951/) (2018)
- [A Benchmark Study on Sentiment Analysis for Software Engineering Research](https://mlsanity.com/paper/1803.06525/) (2018)
- [Tuned Models of Peer Assessment in MOOCs](https://mlsanity.com/paper/1307.2579/) (2013)
- [SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification](https://mlsanity.com/paper/1608.05129/) (2016)
- [Leveraging Peer Feedback to Improve Visualization Education](https://mlsanity.com/paper/2001.07549/) (2020)

## Abstract (arXiv)

Accurately grading open-ended assignments in large or massive open online courses (MOOCs) is non-trivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has 1) leveraged sentiment analysis in the peer-review process to inform or validate grades or 2) utilized aspect extraction to craft a review form from what students actually communicated. Our work utilizes, rather than discards, student data from review form comments to deliver better information to the instructor. In this work, we detail the process by which we create our domain-dependent lexicon and aspect-informed review form as well as our entire sentiment analysis algorithm which provides a fine-grained sentiment score from text alone. We end by analyzing validity and discussing conclusions from our corpus of over 6800 peer reviews from nine courses to understand the viability of sentiment in the classroom for increasing the information from and reliability of grading open-ended assignments in large courses.