Reaction GIFs Offer A New Key To Emotion Recognition In NLP

New research out of China is offering a novel method for Natural Language Processing (NLP) to perform sentiment analysis on social media forums and language research datasets – by categorizing and labeling animated GIFs that are posted in response to text announcements.

The researchers, led by Boaz Shmueli of National Tsing Hua University at Taiwan, have used Twitter’s in-built database of reaction GIFs as an index to quantify the affective state of a user’s response, obviating the need to negotiate multiple language responses, the challenge of detecting sarcasm, or of identifying core emotional temperature from ambiguous or excessively brief responses.

Clicking the ‘GIF’ button when composing a Twitter post offers a standard set of labeled animated GIFs that are potentially easier for NLP to parse into ‘identified’ emotions than plain-text language.

The paper characterizes the use of reaction GIFs in this way as ‘a new type of label, not yet available in NLP emotion datasets’, and notes that existing datasets either use the dimensional model of emotion or the discrete emotions model, neither of which offers this kind of insight.

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