# Sentiment Analysis in Digital Spaces: An Overview of Reviews

arXiv: https://arxiv.org/abs/2310.19687 · HTML: https://mlsanity.com/paper/2310.19687/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 213 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 29 of 29)

- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research](https://mlsanity.com/paper/2005.00357/) (2020)
- [Computational Sociolinguistics: A Survey](https://mlsanity.com/paper/1508.07544/) (2015)
- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances](https://mlsanity.com/paper/2310.07343/) (2023)
- [Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation](https://mlsanity.com/paper/2002.08267/) (2020)
- [Preregistering NLP Research](https://mlsanity.com/paper/2103.06944/) (2021)
- [The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers](https://mlsanity.com/paper/1612.01556/) (2016)
- [Characterizing English Variation across Social Media Communities with BERT](https://mlsanity.com/paper/2102.06820/) (2021)
- [The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning](https://mlsanity.com/paper/2203.06498/) (2022)
- [A Survey on Sentiment and Emotion Analysis for Computational Literary Studies](https://mlsanity.com/paper/1808.03137/) (2018)
- [The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis](https://mlsanity.com/paper/2310.12318/) (2023)
- [Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter](https://mlsanity.com/paper/2004.06986/) (2020)
- [Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews](https://mlsanity.com/paper/2010.13814/) (2020)
- [Over a Decade of Social Opinion Mining: A Systematic Review](https://mlsanity.com/paper/2012.03091/) (2020)
- [A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews](https://mlsanity.com/paper/2008.10282/) (2020)
- [Natural Language Processing, Sentiment Analysis and Clinical Analytics](https://mlsanity.com/paper/1902.00679/) (2019)
- [Towards transparency in NLP shared tasks](https://mlsanity.com/paper/2105.05020/) (2021)

## Abstract (arXiv)

Sentiment analysis (SA) is commonly applied to digital textual data, revealing insight into opinions and feelings. Many systematic reviews have summarized existing work, but often overlook discussions of validity and scientific practices. Here, we present an overview of reviews, synthesizing 38 systematic reviews, containing 2,275 primary studies. We devise a bespoke quality assessment framework designed to assess the rigor and quality of systematic review methodologies and reporting standards. Our findings show diverse applications and methods, limited reporting rigor, and challenges over time. We discuss how future research and practitioners can address these issues and highlight their importance across numerous applications.