In the preceding chapters of this book, experiments were presented to investigate the impact of preprocessing on enhancing the performance of classification models for fake news detection. The objective was to assess how data-oriented enhancements could improve model outcomes. Various transfer learning strategies based on pretrained transformer-based models were also explored. The effectiveness of transfer learning was examined as a potential solution for fake news and hate speech detection—two tasks often hindered by the scarcity of large annotated data sets. This chapter provides an overview of the experiments conducted to examine the relationship between claim statements and sentiment. The aim is to assess how sentiment analysis can contribute to the detection of fake news. The problem and corresponding research objectives are introduced in Sect. 5.1. In Sect. 5.2, the use of sentiment scores to extract claim statements from news documents is investigated. Section 5.3 addresses the incorporation of non-textual features, including sentiment scores, into transformer-based pretrained models for claim check-worthiness detection. Finally, the chapter is summarized in Sect. 5.4.