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

TL;DR

This paper presents a hidden conditional random fields model to classify movie review transcripts by capturing opinion dynamics, using linguistic features and outperforming other approaches on a specific corpus.

Contribution

The novel application of hidden conditional random fields to model opinion dynamics in long unsegmented audio reviews with linguistic features.

Findings

Achieved an F1-score of 82% on the ICT-MMMO corpus.

Outperformed logistic regression and RNN approaches.

Discussed adaptation of word embeddings from written to spoken reviews.

Abstract

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.