Even though Deep Learning (DL) models are increasingly used in recent years to develop trading agents, most of them solely rely on a restricted set of input information, e.g., price time-series. However, this is in contrast with the information that is usually available to human traders that, apart from relying on price information, also take into account their prior knowledge, sentiment that is expressed regarding various markets and assets, as well as general news and forecasts. In this paper, we examine whether the use of sentiment information, as extracted by various online sources, including news articles, is beneficial when training DL agents for trading. More specifically, we provide an extensive evaluation that includes several different configurations and models, ranging from Multi-layer Perceptrons (MLPs) to Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), examining the impact of using sentiment information when developing DL models for trading applications. Apart from demonstrating that sentiment can indeed lead to improved trading efficiency, we also provide further insight on the use of sentiment-enriched data sources for cryptocurriences, such as Bitcoin, where its seems that sentiment information might actually be a stronger predictor compared to the information provided by the actual price time-series.