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

Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields