Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling.

ABSTRACT: The article presents a discriminative approach to complement the unsupervised probabilistic nature of topic modelling. The framework transforms the probabilities of the topics per document into class-dependent deep learning models that extract highly discriminatory features suitable for classification. The framework is then used for sentiment analysis with minimum feature engineering. The approach transforms the sentiment analysis problem from the word/document domain to the topics domain making it more robust to noise and incorporating complex contextual information that are not represented otherwise. A stacked denoising autoencoder (SDA) is then used to model the complex relationship among the topics per sentiment with minimum assumptions. To achieve this, a distinct topic model and SDA

SUBMITTER: Al Moubayed N 

PROVIDER: S-EPMC7924555 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature