In recent years, pre-trained language models have demonstrated exceptional performance across various natural language processing (NLP) tasks. One fundamental component of these models is the self-attention mechanism, which has played a vital role in capturing meaningful relationships between tokens. However, a question still remains as to whether injecting lexical features into the self-attention mechanism can further enhance the understanding and performance of language models. This paper presents a novel approach for injecting semantic-polarity knowledge, referred to as Sentiment Lexical Attention, directly into the self-attention mechanism of Transformer-based models. The primary goal is to improve performance on sentiment classification task. Our approach involves consistently injecting Sentiment Lexical Attention derived from the lexicon corpus into the attention scores throughout the training process. We empirically evaluate our method on the NSMC sentiment classification benchmark, showcasing significant performance improvements and achieving state-of-the-art results. Furthermore, our approach demonstrates robustness and effectiveness in out-of-domain tasks, indicating its potential for broad applicability. Additionally, we analyze the impact of Sentiment Lexical Attention on the view of the CLS token’s attention distribution. Our method offers a fresh perspective on synergizing lexical features and attention scores, thereby encouraging further investigations in the realm of knowledge injection utilizing the lexical features.