In recent years, research on emotion classification based on physiological signals has actively attracted scholars’ attention worldwide. Several studies and experiments have been conducted to analyze human emotions based on physiological signals, including the use of electrocardiograms (ECGs), electroencephalograms (EEGs), and photoplethysmograms (PPGs). Although the achievements with ECGs and EEGs are progressive, reaching higher accuracies over 90%, the number of studies utilizing PPGs are limited and their accuracies are relatively lower than other signals. One of the difficulties in studying PPGs for emotional analysis is the lack of open datasets (there is a single dataset to the best of the authors). This study introduces a new PPG dataset for emotional analysis. A total of 72 PPGs were recorded from 18 participants while watching short video clips and analyzed in time and frequency domains. Moreover, emotional classification accuracies with the presented dataset were presented with various neural network structures. The results prove that this dataset can be used for further emotional analysis with PPGs.