In this paper, we have designed a character-level pre-trained language model for extracting support phrases from tweets based on the sentiment label. We also propose a character-level ensemble model designed by properly blending Pre-trained Contextual Embeddings (PCE) models- RoBERTa, BERT, and ALBERT along with Neural network models- RNN, CNN and WaveNet at different stages of the model. For a given tweet and associated sentiment label, our model predicts the span of phrases in a tweet that prompts the particular sentiment in the tweet. In our experiments, we have explored various model architectures and configuration for both single as well as ensemble models. We performed a systematic comparative analysis of all the model’s performance based on the Jaccard score obtained. The best performing ensemble model obtained the highest Jaccard scores of 73.5, giving it a relative improvement of 2.4% over the best performing single RoBERTa based character-level model, at 71.5(Jaccard score).