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