Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields
This work addresses opinion classification in spoken movie reviews, which is an incremental improvement for natural language processing applications.
The paper tackled opinion detection in movie review transcripts by using hidden conditional random fields to capture opinion dynamics, achieving an F1-score of 82% on the ICT-MMMO corpus, outperforming logistic regression and recurrent neural networks.
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.