This is a crucial factor to take into account when classifying emotions: intuitively, the categories themselves can radically change depending on the cultural background. The cultural variation of emotions has also been studied. Russel is not the first scholar to use a circular layout to represent emotions (for instance, it was already used in ): indeed, this kind of circumplex representation became very popular over time, since it is suitable to spatially represent emotions on a continuous space.
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According to Russel, the full spectrum of emotions can be meaningfully placed along the resulting circumference. Russel’s model posits that affect states are defined by dimensions that are not independent: emotions can be represented as points in a space whose main orthogonal axes represent their degree of arousal and of pleasure/displeasure. One of the most famous dimensional models is Russel’s circumplex model of emotions. Such frameworks are commonly divided into categorical models, based on a finite set of labels, and dimensional models, that position data points as continuous values in an N-dimensional vector space of emotions. Sentences, conversations, posts, tweets and many other pieces of text can be labelled according to a variety of schemes, that refer to as many psychological theoretical frameworks. The recent availability of massive textual corpora has enhanced an extensive research over the emotional dimension underlying human-produced texts. We substantiate our claim that PyPlutchik outperforms other classic visualisations when displaying Plutchik emotions and we showcase a few examples that display our module’s most compelling features. Notably, PyPlutchik allows users to display also primary, secondary, tertiary and opposite dyads in a compact, intuitive way.
PyPlutchik draws the Plutchik’s flower with each emotion petal sized after how much that emotion is detected or annotated in the corpus, also representing three degrees of intensity for each of them.
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For any enquiry about usage or installation feel free to contact the corresponding author, a Python module specifically designed for the visualisation of Plutchik’s emotions in texts or in corpora. We introduce PyPlutchik the Pyplutchik package is available as a Github repository ( ) or through the installation commands pip or conda. The most common layouts fail to preserve both features, not to mention the need of visually allowing comparisons between different corpora in a blink of an eye, that is hard with basic design solutions. The classic representation of the Plutchik’s wheel follows the principles of proximity and opposition between pairs of emotions: spatial proximity in this model is also a semantic proximity, as adjacent emotions elicit a complex emotion (a primary dyad) when triggered together spatial opposition is a semantic opposition as well, as positive emotions are opposite to negative emotions. Visualisation of such emotions is traditionally carried out using the most popular layouts, as bar plots or tables, which are however sub-optimal. Related researches range from annotation tasks description to emotions detection tools. The increasing availability of textual corpora and data fetched from social networks is fuelling a huge production of works based on the model proposed by psychologist Robert Plutchik, often referred simply as the “Plutchik Wheel”.
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