# Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons

arXiv: https://arxiv.org/abs/2210.07381 · HTML: https://mlsanity.com/paper/2210.07381/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [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)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Modeling Protagonist Emotions for Emotion-Aware Storytelling](https://mlsanity.com/paper/2010.06822/) (2020)
- [Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/2109.08256/) (2021)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)
- [Emotion Dynamics in Movie Dialogues](https://mlsanity.com/paper/2103.01345/) (2021)
- [Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons](https://mlsanity.com/paper/2011.03492/) (2020)
- [From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales](https://mlsanity.com/paper/1309.5909/) (2013)

## Built on by (most cited first, 6 of 6)

- [Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across Age](https://mlsanity.com/paper/2306.05387/) (2023)
- [Affect as a proxy for literary mood](https://mlsanity.com/paper/2304.02894/) (2023)
- [The Impact of Incumbent/Opposition Status and Ideological Similitude on Emotions in Political Manifestos](https://mlsanity.com/paper/2305.08383/) (2023)
- [The Effects of Political Martyrdom on Election Results: The Assassination of Abe](https://mlsanity.com/paper/2305.18004/) (2023)
- [I or Not I: Unraveling the Linguistic Echoes of Identity in Samuel Beckett's "Not I" Through Natural Language Processing](https://mlsanity.com/paper/2410.09608/) (2024)
- [Traversing Emotional Landscapes and Linguistic Patterns in Bernard-Marie Kolt`es' Plays: An NLP Perspective](https://mlsanity.com/paper/2410.09609/) (2024)

## Abstract (arXiv)

Automatically generated emotion arcs -- that capture how an individual or a population feels over time -- are widely used in industry and research. However, there is little work on evaluating the generated arcs. This is in part due to the difficulty of establishing the true (gold) emotion arc. Our work, for the first time, systematically and quantitatively evaluates automatically generated emotion arcs. We also compare two common ways of generating emotion arcs: Machine-Learning (ML) models and Lexicon-Only (LexO) methods. Using a number of diverse datasets, we systematically study the relationship between the quality of an emotion lexicon and the quality of the emotion arc that can be generated with it. We also study the relationship between the quality of an instance-level emotion detection system (say from an ML model) and the quality of emotion arcs that can be generated with it. We show that despite being markedly poor at instance level, LexO methods are highly accurate at generating emotion arcs by aggregating information from hundreds of instances. This has wide-spread implications for commercial development, as well as research in psychology, public health, digital humanities, etc. that values simple interpretable methods and disprefers the need for domain-specific training data, programming expertise, and high-carbon-footprint models.