  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.
