Affective, semantic, frequency, and descriptive norms for 107 face emojis

Abstract

We introduce a novel dataset of affective, semantic, and descriptive norms for all facial emojis at the point of data collection. We gathered and examined subjective ratings of emojis from 138 German speakers along five essential dimensions: valence, arousal, familiarity, clarity, and visual complexity. Additionally, we provide absolute frequency counts of emoji use, drawn from an extensive Twitter corpus, as well as a much smaller WhatsApp database. Our results replicate the well-established quadratic relationship between arousal and valence of lexical items, also known for words. We also report associations among the variables: for example, the subjective familiarity of an emoji is strongly correlated with its usage frequency, and positively associated with its emotional valence and clarity of meaning. We establish the meanings associated with face emojis, by asking participants for up to three descriptions for each emoji. Using this linguistic data, we computed vector embeddings for each emoji, enabling an exploration of their distribution within the semantic space. Our description-based emoji vector embeddings not only capture typical meaning components of emojis, such as their valence, but also surpass simple definitions and direct emoji2vec models in reflecting the semantic relationship between emojis and words. Our dataset stands out due to its robust reliability and validity. This new semantic norm for face emojis impacts the future design of highly controlled experiments focused on the cognitive processing of emojis, their lexical representation, and their linguistic properties.

Keywords: Arousal; Emoji; Frequency; Norming study; Subjective rating; Valence; Visual complexity; Word embeddings.

Fig. 1

Sample familiarity rating screenshot. The task beneath the emoji states “How familiar is the emoji from your daily life? The more often you see an emoji, the more familiar it is.” The scale on the slider ranges from “never seen” to “seen very often”

Fig. 2

Face emoji frequency (log scale) in a large corpus of German tweets (2022)

Fig. 3

Distribution of ratings for all measures

Fig. 4

Correlation of subjective familiarity and absolute frequency (Twitter)

Fig. 5

Correlations between all rating measures. The diagonals show kernel density estimates (KDE) for each variable, which represent the distribution of the data using a continuous probability density curve

Fig. 6

Mean arousal and valence of the face emojis

Fig. 7

Subjective arousal and valence ratings of emojis compared with arousal and valence words norms of the emojis’ semantic descriptions

Fig. 8

Correlation between the mean clarity rating and the sense variation for each emoji

Fig. 9

T-SNE plot of direct emoji embeddings

Fig. 10

T-SNE plot of emoji description embeddings (average embeddings for our solicited descriptions for each emoji)

Fig. 11

T-SNE plot of emoji description embeddings (identical to Fig. 10), colored by the emoji’s mean valence rating

Fig. 12

T-SNE plot of direct emoji embeddings (identical to Fig. 9), colored by the emoji’s mean valence rating

Fig. 13

Self-disclosed WhatsApp (left image) and emoji usage (right image) by gender

Fig. 14

T-SNE plot of emoji description embeddings together with the word embeddings of the most frequently used token describing each emoji

Fig. 15

Visual complexity ratings for face emojis

Fig. 16

Familiarity ratings for face emojis

Fig. 17

Clarity ratings for face emojis

Fig. 18

Arousal ratings for face emojis

Fig. 19

Valence ratings for face emojis