# On Positivity Bias in Negative Reviews

arXiv: https://arxiv.org/abs/2106.12056 · HTML: https://mlsanity.com/paper/2106.12056/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models](https://mlsanity.com/paper/1907.13528/) (2019)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://mlsanity.com/paper/1804.09635/) (2018)
- [Evaluating and Characterizing Human Rationales](https://mlsanity.com/paper/2010.04736/) (2020)
- [Human language reveals a universal positivity bias](https://mlsanity.com/paper/1406.3855/) (2014)
- [Author's Sentiment Prediction](https://mlsanity.com/paper/2011.06128/) (2020)
- [Characterizing the Value of Information in Medical Notes](https://mlsanity.com/paper/2010.03574/) (2020)

## Built on by (most cited first, 3 of 3)

- [Sentiment and structure in word co-occurrence networks on Twitter](https://mlsanity.com/paper/2110.00587/) (2021)
- [Augmenting semantic lexicons using word embeddings and transfer learning](https://mlsanity.com/paper/2109.09010/) (2021)
- [Casual as an Anchor: Resolving Supervision Misalignment in Formality Transfer Dataset](https://mlsanity.com/paper/2605.29365/) (2026)

## Abstract (arXiv)

Prior work has revealed that positive words occur more frequently than negative words in human expressions, which is typically attributed to positivity bias, a tendency for people to report positive views of reality. But what about the language used in negative reviews? Consistent with prior work, we show that English negative reviews tend to contain more positive words than negative words, using a variety of datasets. We reconcile this observation with prior findings on the pragmatics of negation, and show that negations are commonly associated with positive words in negative reviews. Furthermore, in negative reviews, the majority of sentences with positive words express negative opinions based on sentiment classifiers, indicating some form of negation.