The topic-sentiment mixture (TSM) model (Mei et al., 2007) can jointly model sentiment and topics by constructing an extra background component and two additional sentiment subtopics on top of the probabilistic latent semantic indexing (pLSI) (Hofmann, 1999).
tSM may suffer from the problem of overfitting the data which is known as a deficiency of pLSI, and postprocessing is also required in order to calculate the sentiment prediction for a document.