Interpreting Emoji with Emoji (2022)

Abstract: We study the extent to which emoji can be used to add interpretability to embeddings of text and emoji. To do so, we extend the POLAR-framework that transforms word embeddings to interpretable counterparts and apply it to word-emoji embeddings trained on four years of messaging data from the Jodel social network. We devise a crowdsourced human judgement experiment to study six usecases, evaluating against words only, what role emoji can play in adding interpretability to word embeddings. That is, we use a revised POLAR approach interpreting words and emoji with words, emoji or both according to human judgement. We find statistically significant trends demonstrating that emoji can be used to interpret other emoji very well.

## introduction word embeddings create a vector-space representation in which words with a similar meaning are in close proximity. existing approaches to make embeddings interpretable, e.g., via contextual @xcite , sparse embeddings @xcite , or learned @xcite transformations @xcite )-all focus on text only. yet, emoji are widely used in casual communication, e.g., online social networks (osn), and are known to extend textual expressiveness, demonstrated to benefit, e.g., sentiment analysis @xcite . ## creating interpretable embeddings we explain next our deployed tool for creating interpretable word-emoji embeddings: po-lar @xcite ; and provide detail on a revised polar extension via projection. ## approach: embedding & polarization we next propose an approach to improve the interpretability of word embeddings by adding emoji. it uses our extended version polar ρ and adds emoji to the polar space by creating word embeddings that include emoji. ## human evaluation approach while we have now created a supposedly interpretable embedding, it remains to be seen how well it is perceived by humans. that is, we next evaluate our two key rqs, discuss significance, and provide further details: rq1) how well does polar ρ with ewso perform in selecting most interpretable dimensions at varying expressiveness of words and emoji? rq2) how well do polar ρ scalar values reflect directions on the differential scales? i) do humans prefer emoji to words? ii) how well do human raters align w.r.t. interpretability? iii) what impact do demographic factors play in interpretability with or without emoji? ## related work no universal meaning of emoji. prior work showed that the interpretation of emoji varies @xcite , also between cultures @xcite . even within the same culture, ambiguity and double meanings of emoji exist @xcite and differences exists on the basis of an individual usage (wiseman and gould, 2018). these observations motivate the need to better understand the meaning of emoji. currently, no data-driven approach exists to make emoji interpretable-a gap that we aim to close. interpretable word embeddings. word embeddings are a common approach to capture meaning; they are a learned vector space representation of text that carries semantic relationships as distances between the embedded words. a rich body of work aims at making word embeddings interpretable, e.g., via contextual @xcite , sparse embeddings @xcite , or learned @xcite transformations @xcite )-all focus on text only. recently, @xcite proposed the po-lar that takes a word embedding as input and creates a new interpretable embedding on a polar subspace. the polar approach is similar to @xcite , but is based on the concept of semantic differentials @xcite for creating a polar subspace. it measures the meaning of abstract concepts by relying on opposing dimensions associated (good vs. bad, hot vs. cold, conservative vs. liberal). in this work, we extend and use polar. emoji embeddings. few works focused on using word embeddings for creating emoji representations, e.g., @xcite or @xcite . @xcite used a vector space skip-gram model to infer the meaning of emoji in twitter data @xcite . yet, the general question if the interpretability of word embeddings can be improved by adding emoji and if different meaning of emoji can be captured remains still open. in this work, we adapt the polar interpretability approach to emoji and study in a human subject experiment if word embeddings can be made interpretable by adding emoji and how emoji can be interpretated by emoji. ## conclusion we raise the question whether we can leverage the expressiveness of emoji to make word embeddings interpretable. thus, we use the polar framework @xcite ) that creates interpretable word embeddings through semantic differentials, polar opposites. we employ a revised polar ρ method that transforms arbitrary word embeddings to interpretable counterparts to which we added emoji. we base our evaluation on an off the shelf word-emoji embedding from a large social media corpus, resulting in an interpretable embedding based on semantic differentials, i.e., antonym lists and polar emoji opposites. via crowdsourced campaigns, we investigate the interpretable word-emoji embedding quality along six use-cases (cf. fig. 1 ): using word-& emoji-polar opposites (or both mixed), to interpret words (w/w, w/e, w/m) and emoji (e/w, e/e, e/m), w.r.t. human interpretability. overall, we find polar ρ 's interpretations w/wo emoji being well in line with human judgement. we show that explaining emoji with emoji (e/e) works statistically significantly best, whereas describing words with emoji (w/e) systematically yields the worst performance. we also find good alignment to human judgement estimating a term's position on differential scales, using the polar ρ -projection. that is, emoji can improve polar ρ 's capability in identifying most interpretable semantic differentials. we have demonstrated how emoji can be used to interpret other emoji using polar ρ . p -z p z p -z p z p -z p z p -z p z