The field of Natural Language Processing (NLP) techniques has progressed rapidly over the recent years. With new advancements in transfer learning and the creation of open-source projects like BERT, solutions and research projects emerged implementing new ideas in a variety of domains, for tasks including text classification or question answering. This research focuses on the task of humor detection and sentiment analysis of comic texts with the use of a fine-tuned BERT model. Never before has a fine-tuned BERT model been used for the task of humor detection in a text coming from an artwork. Comic text features domain-specific language, affecting the meaning and structure of commonly used grammar and vocabulary. This may differ from the language people use every day, and from the language the existing humor classifiers used for training. This research contributes to the NLP field with new models and datasets for humor detection and sentiment analysis, and reports on techniques to improve the training times and the accuracy of a pre-trained BERT model on small datasets. The proposed solution trained on comic datasets outperforms the chosen baselines and could be used as a reliable classifier in the given domain. Moreover, the results indicate that techniques reducing the training time of the model can positively contribute to its performance.