Bayesian Deep Convolution Belief Networks for Subjectivity Detection

Abstract

Subjectivity detection aims to distinguish natural language as either opinionated (positive or negative) or neutral. In word vector based convolutional neural network models, a word meaning is simply a signal that helps to classify larger entities such as a document. Previous works do not usually consider prior distribution when using sliding windows to learn word embedding's and, hence, they are unable to capture higher-order and long-range features in text. In this paper, we employ dynamic Gaussian Bayesian networks to learn significant network motifs of words and concepts. These motifs are used to pre-train the convolutional neural network and capture the dynamics of discourse across several sentences.