Detecting emotions and affect in text has received enormous attention in recent years, and yet majority of the works in this area reduce the nuanced emotional responses into ‘positive’, ‘negative’ and ‘neutral’. In this paper, we introduce a novel multi-dimensional affect in poetry (POCA) dataset for sentiment analysis annotated using the Geneva Emotion Wheel (GEW), to capture and analyse the multi-dimensional affect evoked in listeners. The POCA dataset is based on poems and their corresponding recitals from an online poetry database where recitals are curated by the website, and performed by the poet or an approved artist. The POCA dataset contains 330 poems (text and audio), from the English language, each of which is annotated across 20 different emotion classes, by 5 listeners. A subset of the dataset (50 poems) have also been annotated by an inlab study by 3 listeners each while their Electrodermal activity (EDA) was being recorded. As a proof of concept, we (i) introduce representative problem formulations to be addressed by machine learning approaches using the POCA dataset, from single emotion recognition (e.g., does this poem evoke joy?) to continuous affect prediction (e.g., what level arousal and valence does this poem evoke?), (ii) provide baseline results for text-based affect recognition using several classification and regression models, and (iii) provide baseline results for EDA-based affect prediction. Our results show that (i) for text-based affect recognition, classical approaches can provide as accurate results as their fine-tuned neural network counterparts, and (ii) in EDA-based affect prediction, in general there is a strong relation between the EDA signals and the self-reported valence and arousal quadrants, while predictions are better for arousal than valence.