# Towards emotion analysis for Alsatian theater

Qinyue Liu, Pablo Ruiz Fabo, Delphine Bernhard

► To cite this version:

Qinyue Liu, Pablo Ruiz Fabo, Delphine Bernhard. Towards emotion analysis for Alsatian theater. Computational Humanities Research (CHR) 2023, Dec 2023, Paris, France. 2023, 10.5281/zen-odo.8404252. hal-04213017

HAL Id: hal-04213017

https://hal.science/hal-04213017v1

Submitted on 5 Jan 2024

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## Emotion analysis

- Complex task often carried out in Computational Literary Studies (Kim & Klinger, 2021)

- **Approaches**

- Rule and lexicon-based (e.g. NRC, Mohammad & Turney, 2013)

- Supervised learning, transfer learning with contextual LM (e.g. Grisot et al., 2022; Dennerlein et al., 2023)

- **Applications**

- Emotion arc computation (Teodorescu et al., 2023)

- Subgenre characterization

- In the case of Alsatian, **low-resource** scenario

## The MeThAL Alsatian theater corpus

- **Alsatian?**

- Germanic varieties from Alsace (Eastern France)

- 77 **plays** (> 600K tokens), TEI-encoded

- 51 without prior electronic text → OCR + corrections

- 27 **authors**

- Minor and major ones

- Variety of origins

- Female and male

- Variety of dramatic **subgenres**

- Rich bibliographic and **character metadata**

- **Social variables:** gender, socioprofessional group, age, ...

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wikidata

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## Emotion analysis for Alsatian

### ELAL: Emotion Lexicon for Alsatian

Bernhard & Ruiz (2022)

- Based on French-Alsatian **bilingual lexica** and **emotion lexica** for French and German (NRC, FEEL) and German (NRC)

- Terms are aggregated into a large **network**

- Emotion scores for the large-resource languages are projected onto Alsatian terms

- Handles Alsatian's huge **orthographic variability**, matching variants with graphic similarity measures (+ manual correction)

- 3,273 entries

- 11,920 Alsatian forms

### EDYTHA: Emotion Dynamics for Theater in Alsatian

- Inspired by TED (Twitter Emotion Dynamics) tool (Vishnubhotla & Mohammad, 2022) with important modifications

- Accepts **TEI-encoded** input

- Integrates Alsatian **downsampling** (Bernhard, 2018). Applies ELAL

lexicon to compute **emotion scores** averaged over corpus chunks

- Creates **subcorpora** based on plays' and character **metadata**

- **Orthographic variant detection** thanks to ELAL

- Emotion term **weighting schemes** based on tf-idf:

- Prevents frequent terms from skewing scores

- **tf-idf configuration** options:

- idf taking each play as a document

- idf taking each character speech turn as a document

- Compares emotions by **character social variables** (e.g. do socially disfavoured characters show more negative emotion?)

## EDYTHA: Outputs

<figure><img src="image_7.png" /><figcaption>Correlation between different emotion scores in the entire MeThAL corpus</figcaption></figure>

https://git.unistra.fr/methal/edytha

## References

Bernhard, D. & Ruiz, P. (2022). ELAL: An Emotion Lexicon for the Analysis of Alsatian Theatre Plays. In *LREC 2022. Dennerlein, K. Schmidt, T & Wolff, C. (2023). Computational emotion classification for genre corpora of German tragedies and comedies from 17th to early 19th century, Digital Scholarship in the Humanities*. Grisot, G. Rebora, S., Herrmann, J. B. & Pennino, F. (2022). Sentiment lexicons or BERT? A comparison of sentiment analysis approaches and their performance. In *Digital Humanities 2022. Kim, & Klinger (2021). A Survey on Sentiment and Emotion Analysis for Computational Literary Studies, Zeitschrift für digitale Geisteswissenschaften*. Mohammad, S., & Turney, P. (2013). Crowdsourcing a Word-Emotion Association Lexicon. *Computational Intelligence*, 29. Ruiz, P., Bernhard, D., Briand, A. & Werner, C. (2024). Computational drama analysis from almost zero electronic text. In M. Andresen & N. Reiter (eds). *Computational Drama Analysis: Reflecting Methods and Interpretations*. Schmidt, T. Burghardt, M., Dennerlein, K. Wolff, C. (2019). Katharsis – A Tool for Computational Drametrics. In *Digital Humanities 2019. Teodorescu, D., Mohammad, S. M. (2023). Generating High-Quality Emotion Arcs For Low-Resource Languages Using Emotion Lexicons*. Vishnubhotla, K., & Mohammad, S. M. (2022). Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada. In *LREC 2022*.

<figure><img src="image_8.png" /><figcaption>Joy vs sadness scores in the corpus comedies</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Emotion scores in plays with the lowest and highest polarity (valence): For each emotion type, percentile with respect to all plays in the selected subcorpus</figcaption></figure>