mlsanityData to 14 Oct 2026

On Positivity Bias in Negative Reviews

arXiv 2106.12056 ↗First posted 22 Jun 2021cs.CL cs.AI cs.CYcited by 3

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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.

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

9 of 37 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019ERASER: A Benchmark to Evaluate Rationalized NLP Modelscited by 530
  4. 2019What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language modelscited by 320
  5. 2018A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applicationscited by 136
  6. 2020Evaluating and Characterizing Human Rationalescited by 36
  7. 2014Human language reveals a universal positivity biascited by 23
  8. 2020Author's Sentiment Predictioncited by 18
  9. 2020Characterizing the Value of Information in Medical Notescited by 12

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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

3 papers.