  Most previous work in music emotion recognition assumes a single or a few
song-level labels for the whole song. While it is known that different emotions
can vary in intensity within a song, annotated data for this setup is scarce
and difficult to obtain. In this work, we propose a method to predict emotion
dynamics in song lyrics without song-level supervision. We frame each song as a
time series and employ a State Space Model (SSM), combining a sentence-level
emotion predictor with an Expectation-Maximization (EM) procedure to generate
the full emotion dynamics. Our experiments show that applying our method
consistently improves the performance of sentence-level baselines without
requiring any annotated songs, making it ideal for limited training data
scenarios. Further analysis through case studies shows the benefits of our
method while also indicating the limitations and pointing to future directions.
