Chaturvedi, Iti, Cambria, Erik, Poria, Soujanya, and Bajpai, Rajiv (2016) Bayesian deep convolution belief networks for subjectivity detection. In: Proceedings of the IEEE International Conference on Data Mining Workshops. 7836765. pp. 916-923. From: ICDMW 2016: 16th International Conference on Data Mining Workshops, 12-15 December 2015, Barcelona, Spain.

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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.

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