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The animated Graphical Interchange Format (GIF) images have been widely usedon social media as an intuitive way of expression emotion. Given theirexpressiveness, GIFs offer a more nuanced and precise way to convey emotions.In this paper, we present our solution for the EmotionGIF 2020 challenge, theshared task of SocialNLP 2020. To recommend GIF categories for unlabeledtweets, we regarded this problem as a kind of matching tasks and proposed alearning to rank framework based on Bidirectional Encoder Representations fromTransformer (BERT) and LightGBM. Our team won the 4th place with a Mean AveragePrecision @ 6 (MAP@6) score of 0.5394 on the round 1 leaderboard.

A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter