EmotionGIF-IITP-AINLPML: Ensemble-based Automated Deep Neural System for predicting category(ies) of a GIF response

TL;DR

This paper presents an ensemble-based deep neural system for predicting categories of GIF responses to tweets, achieving high recall scores in a shared task.

Contribution

It introduces attention-based Bi-GRU models and ensemble techniques for improved GIF response categorization in social media analysis.

Findings

Achieved top Mean Recall scores of 52.92% and 53.80% in two rounds.

Developed models using tweet and reply text for better prediction.

Ensemble approach improved overall classification performance.

Abstract

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.