EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 2.
3 papers.
- 2022Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verificationcited by 6
- 2021SocialNLP EmotionGIF 2020 Challenge Overview: Predicting Reaction GIF Categories on Social Mediacited by 1
- 2022Track2Vec: fairness music recommendation with a GPU-free customizable-driven frameworkcited by 0
- 2022Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework29 Oct 2022
- 2022Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification26 Jan 2022
- 2021SocialNLP EmotionGIF 2020 Challenge Overview: Predicting Reaction GIF Categories on Social Media24 Feb 2021