  Deep Learning models have shown very promising results in automatically
composing polyphonic music pieces. However, it is very hard to control such
models in order to guide the compositions towards a desired goal. We are
interested in controlling a model to automatically generate music with a given
sentiment. This paper presents a generative Deep Learning model that can be
directed to compose music with a given sentiment. Besides music generation, the
same model can be used for sentiment analysis of symbolic music. We evaluate
the accuracy of the model in classifying sentiment of symbolic music using a
new dataset of video game soundtracks. Results show that our model is able to
obtain good prediction accuracy. A user study shows that human subjects agreed
that the generated music has the intended sentiment, however negative pieces
can be ambiguous.
