This paper introduces an improved method for emotion classification through the integration of emotion lexicons and pre-trained word embeddings. The proposed method utilizes semantically similar features to reconcile the semantic gap between words and emotions. The proposed approach is compared against three baselines for predicting Ekman’s emotions at the document level on the GoEmotions dataset. The effectiveness of the proposed approach is assessed using standard evaluation metrics, which show at least a 5% gain in performance over baselines.