Bayesian deep convolution belief networks for subjectivity detection
Conference Publication ResearchOnline@JCUAbstract
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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Publication Name
IEEE International Conference on Data Mining Workshops, ICDMW
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ISBN/ISSN
2375-9259
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Pages Count
8
Location
Barcelona, Spain
Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
Piscataway, NJ, USA
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DOI
10.1109/ICDMW.2016.0134