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

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.

The importance of fillers for text representations of speech transcripts

“…* Equal contribution So far, the information carried by fillers has only been studied using hand crafted features, for example in Le Grezause (2017); Saini (2017); Dinkar et al (2020). Besides, Barriere et al (2017) show that pre-trained word embeddings such as Word2vec (Mikolov et al, 2013), have poor representation of spontaneous speech words such as "uh", as they are trained on written text and do not carry the same meaning as when used in speech. We address the matter of representing fillers with deep contextualised word representations (Devlin et al, 2019), and investigate their usefulness in NLP tasks for spoken language, without handcrafting features.…”

Social Influence of Competing Groups and Leaders in Opinion Dynamics

“…Social learning has been described as "the social aspect of belief and opinion formation" (Acemoglu and Ozdaglar 2011) and as such is highly relevant to many important economic phenomena including the adoption of new products and services, spread of new technologies or innovations, financial contagion on stock markets and word-of-mouth job search. Social learning models have been used to explore diffusion of innovations (Martins et al 2009), product adoption (Ruf et al 2017), the emergence of fads and fashions through herding of beliefs and information cascades (Banerjee 1992), word of mouth learning (Banerjee and Fudenberg 2004;Ellison and Fudenberg 1995), advertising and marketing (Schulze 2003;Sznajd-Weron and Weron 2003) and movie reviews (Barriere et al 2017).…”