PyPlutchik: Visualising and comparing emotion-annotated corpora

Abstract

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”. Related researches range from annotation tasks description to emotions detection tools. Visualisation of such emotions is traditionally carried out using the most popular layouts, as bar plots or tables, which are however sub-optimal. 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. 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. We introduce PyPlutchik the Pyplutchik package is available as a Github repository (http://github.com/alfonsosemeraro/pyplutchik) or through the installation commands pip or conda. 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. 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. Notably, PyPlutchik allows users to display also primary, secondary, tertiary and opposite dyads in a compact, intuitive way. 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.

Emotional profiling and cognitive networks unravel how mainstream and alternative press framed AstraZeneca, Pfizer and COVID-19 vaccination campaigns

“…This methodology yields a numeric score for each emotion, which is an indication of how much that emotion is under-represented or over-represented. Figures 1 D and 4 B have been generated using a slight modification of the visualisation library PyPlutchik 74 , which allows for a quantitative representation of the Plutchik’s wheel of emotions. Petals were sized after the z-score of how much an emotion has been detected in the TFMNs against a neutral baseline, and coloured only when they were at greater than 1.96 (or lower that − 1.96), making it simple to identify emotions significantly over/under-expressed.…”

EmoAtlas: An emotional profiling tool merging psychological lexicons, artificial intelligence and network science

“…In turn, this can change the intensity with which a given text elicits one or more emotions 12 . The collection of emotions elicited with different intensities by a text represents the "emotional profile" of the latter 11,[13][14][15][16] , e.g., a text might elicit strong levels of fear and anger, together with lower levels of disgust. Levels might be quantified in terms of how many words elicit those emotions compared to a null model 11,15 .…”

DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

“…Similarly, DASentimental could be applied to track the change of explicit expression of depression, anxiety, and stress over history, quantified through the emotions of modern individuals. This would highlight changes in norms towards emotional expression and historical events (e.g., "pandemic"), thus complementing other recent approaches in cognitive network science [9,30,[59][60][61] and sentiment/emotional profiling [51,55,62,63] by bringing to the table a quantitative, automatic quantification of depression, anxiety, and stress in texts.…”