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. An animated GIF response to a user post.

In this paper, we describe the systems submitted by our IITP-AINLPML team in the shared task of SocialNLP 2020, EmotionGIF 2020, on predicting the category(ies) of a GIF response for a given unlabelled tweet. For the round 1 phase of the task, we propose an attention-based Bi-directional GRU network trained on both the tweet (text) and their replies (text wherever available) and the given category(ies) for its GIF response. In the round 2 phase, we build several deep neural-based classifiers for the task and report the final predictions through a majority voting based ensemble technique. Our proposed models attain the best Mean Recall (MR) scores of 52.92% and 53.80% in round 1 and round 2, respectively.