  In this paper, the main goal is to detect a movie reviewer's opinion using
hidden conditional random fields. This model allows us to capture the dynamics
of the reviewer's opinion in the transcripts of long unsegmented audio reviews
that are analyzed by our system. High level linguistic features are computed at
the level of inter-pausal segments. The features include syntactic features, a
statistical word embedding model and subjectivity lexicons. The proposed system
is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82\%, which is
better than logistic regression and recurrent neural network approaches. We
also offer a discussion that sheds some light on the capacity of our system to
adapt the word embedding model learned from general written texts data to
spoken movie reviews and thus model the dynamics of the opinion.
