# EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods

arXiv: https://arxiv.org/abs/2007.02259 · HTML: https://mlsanity.com/paper/2007.02259/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification](https://mlsanity.com/paper/2201.11664/) (2022)
- [SocialNLP EmotionGIF 2020 Challenge Overview: Predicting Reaction GIF Categories on Social Media](https://mlsanity.com/paper/2102.12073/) (2021)
- [Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework](https://mlsanity.com/paper/2210.16590/) (2022)

## Abstract (arXiv)

This paper provides a method to classify sentiment with robust model based ensemble methods. We preprocess tweet data to enhance coverage of tokenizer. To reduce domain bias, we first train tweet dataset for pre-trained language model. Besides, each classifier has its strengths and weakness, we leverage different types of models with ensemble methods: average and power weighted sum. From the experiments, we show that our approach has achieved positive effect for sentiment classification. Our system reached third place among 26 teams from the evaluation in SocialNLP 2020 EmotionGIF competition.