In an era of increasing user activity on social media, the automatic detection of emotions and sarcasm in online communication is gaining importance. The aim of this study was to develop a hybrid method for classifying emotions, sarcasm, and sentiment in content published in Polish on platform X (formerly Twitter). The proposed solution combines a transformer-based language model (HerBERT) with lexical analysis, utilizing an Emotion and Sentiment Dictionary based on information drawn from plWordNet. Research conducted on a Polish-language dataset showed that the hybrid approach using the dictionary improves classification precision compared to methods relying only on transformers. Additionally, data balancing techniques contributed to a slight improvement in the F1-Score metric of the HerBERT-base model on the dataset.