Understanding and classifying the emotional content of dream descriptions is a unique challenge that sits between psychology and natural language processing. This thesis tackles this issue by creating a machine learning pipeline that can automatically predict sentiment labels from free-text dream narratives. The main method uses handcrafted lexical-semantic features for sentiment classification. Specifically, Term Frequency–Inverse Document Frequency (TF-IDF) extracts lexical patterns, while Empath, a psychologically grounded lexicon, provides high-level semantic signals. To address extreme class imbalance in the labeled data, a targeted resampling strategy based on SMOTE is used to ensure balanced representation across all sentiment categories. A Multi-Layer Perceptron (MLP) classifier is trained on the engineered features and evaluated using both 5-fold stratified cross-validation and Leave-One-Out Cross-Validation (LOOCV).