# Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text

arXiv: https://arxiv.org/abs/2005.11882 · HTML: https://mlsanity.com/paper/2005.11882/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 26 of 26)

- [The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress](https://mlsanity.com/paper/2104.07123/) (2021)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [MultiModal Bias: Introducing a Framework for Stereotypical Bias Assessment beyond Gender and Race in Vision Language Models](https://mlsanity.com/paper/2303.12734/) (2023)
- [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021)
- [ProTo: Program-Guided Transformer for Program-Guided Tasks](https://mlsanity.com/paper/2110.00804/) (2021)
- [Emotion Dynamics in Movie Dialogues](https://mlsanity.com/paper/2103.01345/) (2021)
- [An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis](https://mlsanity.com/paper/2303.07201/) (2023)
- [Abstractive Summarization of Large Document Collections Using GPT](https://mlsanity.com/paper/2310.05690/) (2023)
- [End-to-end View Synthesis via NeRF Attention](https://mlsanity.com/paper/2207.14741/) (2022)
- [All rivers run into the sea: Unified Modality Brain-like Emotional Central Mechanism](https://mlsanity.com/paper/2407.15590/) (2024)
- [Bias in, Bias out: Annotation Bias in Multilingual Large Language Models](https://mlsanity.com/paper/2511.14662/) (2025)
- [A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification](https://mlsanity.com/paper/2101.04017/) (2021)
- [Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns](https://mlsanity.com/paper/2201.07538/) (2022)
- [Evaluation of LLMs-based Hidden States as Author Representations for Psychological Human-Centered NLP Tasks](https://mlsanity.com/paper/2503.00124/) (2025)
- [Cognitive networks identify the content of English and Italian popular posts about COVID-19 vaccines: Anticipation, logistics, conspiracy and loss of trust](https://mlsanity.com/paper/2103.15909/) (2021)
- [The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisions](https://mlsanity.com/paper/2602.17185/) (2026)
- [The Application of Affective Measures in Text-based Emotion Aware Recommender Systems](https://mlsanity.com/paper/2305.04796/) (2023)
- [Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination](https://mlsanity.com/paper/2401.05254/) (2024)
- [Artificial Intelligence for the Internal Democracy of Political Parties](https://mlsanity.com/paper/2405.09529/) (2024)
- [LogSigma at SemEval-2026 Task 3: Uncertainty-Weighted Multitask Learning for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2603.24896/) (2026)
- [Evolution of emotion semantics](https://mlsanity.com/paper/2108.02887/) (2021)
- [Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews](https://mlsanity.com/paper/2111.06070/) (2021)
- [Analysing the Greek Parliament Records with Emotion Classification](https://mlsanity.com/paper/2205.12012/) (2022)
- [The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning](https://mlsanity.com/paper/2304.09914/) (2023)
- [Classification of Hope in Textual Data using Transformer-Based Models](https://mlsanity.com/paper/2511.12874/) (2025)
- [AfriStereo: A Culturally Grounded Dataset for Evaluating Stereotypical Bias in Large Language Models](https://mlsanity.com/paper/2511.22016/) (2025)

## Abstract (arXiv)

Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.