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# Linguistic Features in Automatic Sarcasm Detection

**Authors:** Martina Ducret, Lauren Kruse, Carlos Martinez, Anna Feldman, Jing Peng · **Year:** 2020 · **Venue:** CLiC-it 2020 **Link:** [aclanthology.org/2020.clicit-1.22](https://aclanthology.org/2020.clicit-1.22/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**0** citations](https://openalex.org/works?filter=cites:W3198030086) openalex · 2026-10-19[Proceedings of the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020)](https://aclanthology.org/volumes/2020.clicit-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.clicit-1.22/)
- [PDF](https://aclanthology.org/2020.clicit-1.22.pdf)
- [DOI 10.4000/books.aaccademia.8485](https://doi.org/10.4000/books.aaccademia.8485)
- [OpenAlex](https://openalex.org/W3198030086)

**Authors (5)**

1. [Martina Ducret](https://openalex.org/A5001005847) [Semantic Scholar](https://www.semanticscholar.org/author/2047323302) · h 0 · 0 citations · 2 works
2. [Lauren Kruse](https://openalex.org/A5011971937) [Semantic Scholar](https://www.semanticscholar.org/author/2094197673) · h 0 · 0 citations · 2 works
3. [Carlos Martinez](https://openalex.org/A5002724492) [ORCID](https://orcid.org/0000-0001-8722-1559) · [Semantic Scholar](https://www.semanticscholar.org/author/2105899838) · h 2 · 15 citations · 22 works
4. [Anna Feldman](https://openalex.org/A5064567279) [ORCID](https://orcid.org/0000-0003-4146-5990) · [Semantic Scholar](https://www.semanticscholar.org/author/145754614) · h 15 · 686 citations · 111 works
5. [Jing Peng](https://openalex.org/A5072128915) [ORCID](https://orcid.org/0000-0001-5490-6228) · [Semantic Scholar](https://www.semanticscholar.org/author/2122806691) · h 31 · 4337 citations · 273 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

On the Twitter part of the FigLang 2020 sarcasm shared task (2,500 sarcastic and 2,500 non-sarcastic training tweets, each with its preceding tweets as context; labels from hashtags), the authors test interpretable features with a Random Forest: counts (characters, punctuation), complexity and style, and psychological features (LIWC categories, valence–arousal–dominance norms, VADER sentiment, a six-emotion classifier), plus the similarity of the response to its context in word embeddings and in emotion. Sarcastic replies shift emotion away from the context (an angry context answered with «joy»), sincere replies stay emotionally close. The response alone reaches F1 0.67 with all features; adding the two previous tweets raises it to 0.70, roughly the shared task's LSTM baseline.

## Ideas

- I1. Add an emotional-shift check to the irony candidates in `prag/src/irony.rs`: a positive reply (joy, praise, thanks) right after a narrated or quoted negative event or an angry line from another character is a context incongruity, the dialogue form of the implicit contrast already in the rule. Polarity of the previous frame comes from Mova's own polarity lexicon and the `polarity` field of the previous sentence. Measure precision on silver lines whose `means` names irony.
- I2. Nothing to take from the feature set itself: LIWC and the VAD norms are licensed resources and the gains are small; Mova's own cue lists and frames carry the same information transparently.

## For Mova

[34. Irony and figurative language](https://md.mova.elamur.ai/free/en/docs/languages/en/34-irony-and-figurative-language/) §4 (cues of irony), §10. Code: `prag/src/irony.rs`, `prag/src/feats.rs` (previous sentence polarity). Measure: irony-candidate precision on silver with and without the shift check.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.4000/books.aaccademia.8485](https://doi.org/10.4000/books.aaccademia.8485)

```
@inproceedings{ducret2020linguistic,
  title = {{Linguistic Features in Automatic Sarcasm Detection}},
  author = {Ducret, Martina and Kruse, Lauren and Martinez, Carlos and Feldman, Anna and Peng, Jing},
  booktitle = {Proceedings of the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020)},
  year = {2020},
  doi = {10.4000/books.aaccademia.8485},
  url = {https://doi.org/10.4000/books.aaccademia.8485}
}
```

- [← previous DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.mova.elamur.ai/free/en/docs/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)
- [next → Modeling Global Syntactic Variation in English Using Dialect Classification](https://md.mova.elamur.ai/free/en/docs/papers/dunn-2019-modeling-global-syntactic-variation-english/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

34 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Hugo Queiroz Abonizio, Janaina Ignacio de Morais, Gabriel Marques Tavares, and Sylvio Barbon Junior. 2020. Language-independent fake news detection: English, portuguese, and spanish mutual features. Future Internet, 12(5):87. [find](https://scholar.google.com/scholar?q=Hugo+Queiroz+Abonizio%2C+Janaina+Ignacio+de+Morais%2C+Gabriel+Marques+Tavares%2C+and+Sylvio+Barbon+Junior%2E+2020%2E+Language%2Dindependent+fake+news+detection%3A+English%2C+portuguese%2C+and+spanish+mutual+features%2E+F)
2. Silvio Amir, Byron C Wallace, Hao Lyu, and Paula Carvalho Mário J Silva. 2016. Modelling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976. [↗](https://arxiv.org/abs/1607.00976)
3. David Bamman and Noah Smith. 2015. Contextualized sarcasm detection on twitter. [find](https://scholar.google.com/scholar?q=David+Bamman+and+Noah+Smith%2E+2015%2E+Contextualized+sarcasm+detection+on+twitter%2E)
4. Konstantin Buschmeier, Philipp Cimiano, and Roman Klinger. 2014. An impact analysis of features in a classification approach to irony detection in product reviews. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 42–49, Baltimore, Maryland. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Konstantin+Buschmeier%2C+Philipp+Cimiano%2C+and+Roman+Klinger%2E+2014%2E+An+impact+analysis+of+features+in+a+classification+approach+to+irony+detection+in+product+reviews%2E+In+Proceedings+of+the+5th+Workshop+o)
5. Yitao Cai, Huiyu Cai, and Xiaojun Wan. 2019. Multimodal sarcasm detection in twitter with hierarchical fusion model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2506–2515. [find](https://scholar.google.com/scholar?q=Yitao+Cai%2C+Huiyu+Cai%2C+and+Xiaojun+Wan%2E+2019%2E+Multimodal+sarcasm+detection+in+twitter+with+hierarchical+fusion+model%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Computational+Ling)
6. Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, and Soujanya Poria. 2019. Towards multimodal sarcasm detection (an obviously perfect paper). arXiv preprint arXiv:1906.01815. [↗](https://arxiv.org/abs/1906.01815)
7. Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010. Semi-supervised recognition of sarcasm in Twitter and Amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pages 107–116, Uppsala, Sweden. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Dmitry+Davidov%2C+Oren+Tsur%2C+and+Ari+Rappoport%2E+2010%2E+Semi%2Dsupervised+recognition+of+sarcasm+in+Twitter+and+Amazon%2E+In+Proceedings+of+the+Fourteenth+Conference+on+Computational+Natural+Language+Learning)
8. Aniruddha Ghosh and Tony Veale. 2016. Fracking sarcasm using neural network. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 161–169, San Diego, California. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Aniruddha+Ghosh+and+Tony+Veale%2E+2016%2E+Fracking+sarcasm+using+neural+network%2E+In+Proceedings+of+the+7th+Workshop+on+Computational+Approaches+to+Subjectivity%2C+Sentiment+and+Social+Media+Analysis%2C+pages+)
9. Aniruddha Ghosh and Tony Veale. 2017. Magnets for sarcasm: Making sarcasm detection timely, contextual and very personal. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 482–491. [find](https://scholar.google.com/scholar?q=Aniruddha+Ghosh+and+Tony+Veale%2E+2017%2E+Magnets+for+sarcasm%3A+Making+sarcasm+detection+timely%2C+contextual+and+very+personal%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Language)
10. Debanjan Ghosh, Alexander R Fabbri, and Smaranda Muresan. 2018. Sarcasm analysis using conversation context. Computational Linguistics, 44(4):755– 792. [find](https://scholar.google.com/scholar?q=Debanjan+Ghosh%2C+Alexander+R+Fabbri%2C+and+Smaranda+Muresan%2E+2018%2E+Sarcasm+analysis+using+conversation+context%2E+Computational+Linguistics%2C+44%284%29%3A755%E2%80%93+792%2E)
11. Debanjan Ghosh, Weiwei Guo, and Smaranda Muresan. 2015. Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1003– 1012, Lisbon, Portugal. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Debanjan+Ghosh%2C+Weiwei+Guo%2C+and+Smaranda+Muresan%2E+2015%2E+Sarcastic+or+not%3A+Word+embeddings+to+predict+the+literal+or+sarcastic+meaning+of+words%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Metho)
12. Debanjan Ghosh, Avijit Vajpayee, and Smaranda Muresan. 2020. A report on the 2020 sarcasm detection shared task. In Proceedings of the Second Workshop on Figurative Language Processing, pages 1– 11, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Debanjan+Ghosh%2C+Avijit+Vajpayee%2C+and+Smaranda+Muresan%2E+2020%2E+A+report+on+the+2020+sarcasm+detection+shared+task%2E+In+Proceedings+of+the+Second+Workshop+on+Figurative+Language+Processing%2C+pages+1%E2%80%93+11%2C+O)
13. Roberto González-Ibáñez, Smaranda Muresan, and Nina Wacholder. 2011. Identifying sarcasm in Twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 581–586, Portland, Oregon, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Roberto+Gonza%CC%81lez%2DIba%CC%81n%CC%83ez%2C+Smaranda+Muresan%2C+and+Nina+Wacholder%2E+2011%2E+Identifying+sarcasm+in+Twitter%3A+A+closer+look%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational+Li)
14. C.J. Hutto and Eric Gilbert. 2015. Vader: A parsimonious rule-based model for sentiment analysis of social media text. [find](https://scholar.google.com/scholar?q=C%2EJ%2E+Hutto+and+Eric+Gilbert%2E+2015%2E+Vader%3A+A+parsimonious+rule%2Dbased+model+for+sentiment+analysis+of+social+media+text%2E)
15. Aditya Joshi, Vinita Sharma, and Pushpak Bhattacharyya. 2015. Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 757–762, Beijing, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Aditya+Joshi%2C+Vinita+Sharma%2C+and+Pushpak+Bhattacharyya%2E+2015%2E+Harnessing+context+incongruity+for+sarcasm+detection%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Computational+Lingu)
16. Anupam Khattri, Aditya Joshi, Pushpak Bhattacharyya, and Mark Carman. 2015. Your sentiment precedes you: Using an author’s historical tweets to predict sarcasm. In Proceedings of the 6th workshop on computational approaches to subjectivity, sentiment and social media analysis, pages 25–30. [find](https://scholar.google.com/scholar?q=Anupam+Khattri%2C+Aditya+Joshi%2C+Pushpak+Bhattacharyya%2C+and+Mark+Carman%2E+2015%2E+Your+sentiment+precedes+you%3A+Using+an+author%E2%80%99s+historical+tweets+to+predict+sarcasm%2E+In+Proceedings+of+the+6th+workshop+on+c)
17. Christine Liebrecht, Florian Kunneman, and Antal van den Bosch. 2013. The perfect solution for detecting sarcasm in tweets #not. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 29–37, Atlanta, Georgia. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Christine+Liebrecht%2C+Florian+Kunneman%2C+and+Antal+van+den+Bosch%2E+2013%2E+The+perfect+solution+for+detecting+sarcasm+in+tweets+%23not%2E+In+Proceedings+of+the+4th+Workshop+on+Computational+Approaches+to+Subje)
18. Ashwin Rajadesingan, Reza Zafarani, and Huan Liu. 2015. Sarcasm detection on twitter: A behavioral modeling approach. In Proceedings of the eighth ACM international conference on web search and data mining, pages 97–106. [find](https://scholar.google.com/scholar?q=Ashwin+Rajadesingan%2C+Reza+Zafarani%2C+and+Huan+Liu%2E+2015%2E+Sarcasm+detection+on+twitter%3A+A+behavioral+modeling+approach%2E+In+Proceedings+of+the+eighth+ACM+international+conference+on+web+search+and+data+m)
19. Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 704–714, Seattle, Washington, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Ellen+Riloff%2C+Ashequl+Qadir%2C+Prafulla+Surve%2C+Lalindra+De+Silva%2C+Nathan+Gilbert%2C+and+Ruihong+Huang%2E+2013%2E+Sarcasm+as+contrast+between+a+positive+sentiment+and+negative+situation%2E+In+Proceedings+of+the+) [our card](https://md.mova.elamur.ai/free/en/docs/papers/prag-2013-riloff-sarcasm-as-contrast-between-positive/)
20. Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018. CARER: Contextualized affect representations for emotion recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3687–3697, Brussels, Belgium. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Elvis+Saravia%2C+Hsien%2DChi+Toby+Liu%2C+Yen%2DHao+Huang%2C+Junlin+Wu%2C+and+Yi%2DShin+Chen%2E+2018%2E+CARER%3A+Contextualized+affect+representations+for+emotion+recognition%2E+In+Proceedings+of+the+2018+Conference+on+Empi)
21. Rossano Schifanella, Paloma de Juan, Joel Tetreault, and Liangliang Cao. 2016. Detecting sarcasm in multimodal social platforms. In Proceedings of the 24th ACM international conference on Multimedia, pages 1136–1145. [find](https://scholar.google.com/scholar?q=Rossano+Schifanella%2C+Paloma+de+Juan%2C+Joel+Tetreault%2C+and+Liangliang+Cao%2E+2016%2E+Detecting+sarcasm+in+multimodal+social+platforms%2E+In+Proceedings+of+the+24th+ACM+international+conference+on+Multimedia%2C+)
22. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2020. RoBERTa: A Robustly Optimized BERT Pretraining Approach. [find](https://scholar.google.com/scholar?q=Yinhan+Liu%2C+Myle+Ott%2C+Naman+Goyal%2C+Jingfei+Du%2C+Mandar+Joshi%2C+Danqi+Chen%2C+Omer+Levy%2C+Mike+Lewis%2C+Luke+Zettlemoyer%2C+and+Veselin+Stoyanov%2E+2020%2E+RoBERTa%3A+A+Robustly+Optimized+BERT+Pretraining+Approach%2E)
23. Yla R. Tausczik and James W. Pennebaker. 2010. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24–54. [find](https://scholar.google.com/scholar?q=Yla+R%2E+Tausczik+and+James+W%2E+Pennebaker%2E+2010%2E+The+psychological+meaning+of+words%3A+Liwc+and+computerized+text+analysis+methods%2E+Journal+of+Language+and+Social+Psychology%2C+29%281%29%3A24%E2%80%9354%2E)
24. Edward Loper and Steven Bird. 2002. NLTK: The Natural Language Toolkit. In In Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics. Philadelphia: Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Edward+Loper+and+Steven+Bird%2E+2002%2E+NLTK%3A+The+Natural+Language+Toolkit%2E+In+In+Proceedings+of+the+ACL+Workshop+on+Effective+Tools+and+Methodologies+for+Teaching+Natural+Language+Processing+and+Computat)
25. Yi Tay, Anh Tuan Luu, Siu Cheung Hui, and Jian Su. 2018. Reasoning with sarcasm by reading in-between. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1010–1020, Melbourne, Australia. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Yi+Tay%2C+Anh+Tuan+Luu%2C+Siu+Cheung+Hui%2C+and+Jian+Su%2E+2018%2E+Reasoning+with+sarcasm+by+reading+in%2Dbetween%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volum)
26. Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. [find](https://scholar.google.com/scholar?q=Scott+M+Lundberg+and+Su%2DIn+Lee%2E+2017%2E+A+unified+approach+to+interpreting+model+predictions%2E+In+I%2E+Guyon%2C+U%2E+V%2E+Luxburg%2C+S%2E+Bengio%2C+H%2E+Wallach%2C+R%2E+Fergus%2C+S%2E+Vishwanathan%2C+and+R%2E+Garnett%2C+editors%2C+Adva)
27. Diana Maynard and Mark Greenwood. 2014. Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pages 4238–4243, Reykjavik, Iceland. European Language Resources Association (ELRA). [find](https://scholar.google.com/scholar?q=Diana+Maynard+and+Mark+Greenwood%2E+2014%2E+Who+cares+about+sarcastic+tweets%3F+investigating+the+impact+of+sarcasm+on+sentiment+analysis%2E+In+Proceedings+of+the+Ninth+International+Conference+on+Language+Re)
28. Rishabh Misra and Prahal Arora. 2019. Sarcasm detection using hybrid neural network. ArXiv, abs/1908.07414. [↗](https://arxiv.org/abs/1908.07414)
29. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+D%2E+Manning%2E+2014%2E+Glove%3A+Global+vectors+for+word+representation%2E+In+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2C+pages+1532%E2%80%931543%2E)
30. Oren Tsur, Dmitry Davidov, and Ari Rappoport. 2010. Icwsm - a great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In ICWSM. The AAAI Press. [find](https://scholar.google.com/scholar?q=Oren+Tsur%2C+Dmitry+Davidov%2C+and+Ari+Rappoport%2E+2010%2E+Icwsm+%2D+a+great+catchy+name%3A+Semi%2Dsupervised+recognition+of+sarcastic+sentences+in+online+product+reviews%2E+In+ICWSM%2E+The+AAAI+Press%2E)
31. Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Well-read students learn better: On the importance of pre-training compact models. arXiv preprint arXiv:1908.08962v2. [↗](https://arxiv.org/abs/1908.08962)
32. Tony Veale and Yanfen Hao. 2010. Detecting ironic intent in creative comparisons. In ECAI 2010 - 19th European Conference on Artificial Intelligence, Lisbon, Portugal, August 16-20, 2010, Proceedings, volume 215 of Frontiers in Artificial Intelligence and Applications, pages 765–770. IOS Press. [find](https://scholar.google.com/scholar?q=Tony+Veale+and+Yanfen+Hao%2E+2010%2E+Detecting+ironic+intent+in+creative+comparisons%2E+In+ECAI+2010+%2D+19th+European+Conference+on+Artificial+Intelligence%2C+Lisbon%2C+Portugal%2C+August+16%2D20%2C+2010%2C+Proceedings%2C)
33. Byron C. Wallace, Do Kook Choe, and Eugene Charniak. 2015. Sparse, contextually informed models for irony detection: Exploiting user communities, entities and sentiment. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1035–1044, Beijing, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Byron+C%2E+Wallace%2C+Do+Kook+Choe%2C+and+Eugene+Charniak%2E+2015%2E+Sparse%2C+contextually+informed+models+for+irony+detection%3A+Exploiting+user+communities%2C+entities+and+sentiment%2E+In+Proceedings+of+the+53rd+Ann)
34. Amy Warriner, Victor Kuperman, and Marc Brysbaert. 2013. Norms of valence, arousal, and dominance for 13,915 english lemmas. Behavior research methods, 45. [find](https://scholar.google.com/scholar?q=Amy+Warriner%2C+Victor+Kuperman%2C+and+Marc+Brysbaert%2E+2013%2E+Norms+of+valence%2C+arousal%2C+and+dominance+for+13%2C915+english+lemmas%2E+Behavior+research+methods%2C+45%2E)