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PyPlutchik: Visualising and comparing emotion-annotated corpora.

Alfonso Semeraro1, Salvatore Vilella1, Giancarlo Ruffo1

  • 1Department of Computer Science, University of Turin, Turin, Italy.

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|September 1, 2021
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Summary
This summary is machine-generated.

PyPlutchik is a Python module for visualizing Robert Plutchik's (Plutchik Wheel) emotions in text corpora. It offers superior visualization compared to traditional plots, enhancing emotion analysis and comparison.

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Area of Science:

  • Computational linguistics
  • Affective computing
  • Data visualization

Background:

  • The increasing availability of textual data necessitates advanced tools for emotion analysis.
  • Robert Plutchik's model (Plutchik Wheel) is widely used for understanding emotion relationships.
  • Traditional visualizations like bar plots and tables inadequately represent the complex relationships in Plutchik's model.

Purpose of the Study:

  • To introduce PyPlutchik, a Python module designed for visualizing Plutchik's emotions.
  • To provide a superior visualization tool that preserves semantic proximity and opposition of emotions.
  • To enable efficient comparison of emotion distributions across different text corpora.

Main Methods:

  • Development of a Python package, PyPlutchik, available via GitHub, pip, or conda.
  • Implementation of visualizations based on Plutchik's flower, with petal size indicating emotion frequency.
  • Representation of three intensity degrees for each emotion and visualization of primary, secondary, tertiary, and opposite dyads.

Main Results:

  • PyPlutchik effectively visualizes emotion intensity and relationships (dyads) from text corpora.
  • The module's "flower" layout preserves the proximity and opposition principles of Plutchik's model.
  • Visualizations allow for intuitive and rapid comparison of emotion distributions across corpora.

Conclusions:

  • PyPlutchik offers a significant improvement over traditional visualization methods for Plutchik's emotion model.
  • The tool facilitates deeper insights into emotion detection and analysis in textual data.
  • PyPlutchik enhances the study of emotions in computational linguistics and affective computing.