Sentiment analysis is a useful problem which could serve a variety of fields from business intelligence to social studies and even health studies. Using SemEval 2022 Task 10 formulation of this problem and taking sequence labeling as our approach, we propose a model which learns the task by finetuning a pretrained transformer, introducing as few parameters (~150k) as possible and making use of precomputed attention values in the transformer. Our model improves shared task baselines on all task datasets.