EAN: 9789811374739

SPRINGERBRIEFS IN COMPUTER SCIENCE VISUAL AND...

This book presents the latest research on hierarchical deep learning for multi-modal sentiment
analysis. Further  it analyses sentiments in Twitter blogs from both textual and visual content
using hierarchical deep learning networks: hierarchical gated feedback recurrent neural
networks (HGFRNNs). Several studies on deep learning have been conducted to date  but most of
the current methods focus on either only textual content  or only visual content. In contrast 
the proposed sentiment analysis model can be applied to any social blog dataset  making the
book highly beneficial for postgraduate students and researchers in deep learning and sentiment
analysis. The mathematical abstraction of the sentiment analysis model is presented in a very
lucid manner. The complete sentiments are analysed by combining text and visual prediction
results. The book's novelty lies in its development of innovative hierarchical recurrent neural
networks for analysing sentiments  stacking of multiple recurrent layers by controlling the
signal flow from upper recurrent layers to lower layers through a global gating unit 
evaluation of HGFRNNs with different types of recurrent units  and adaptive assignment of
HGFRNN layers to different timescales. Considering the need to leverage large-scale social
multimedia content for sentiment analysis  both state-of-the-art visual and textual sentiment
analysis techniques are used for joint visual-textual sentiment analysis. The proposed method
yields promising results from Twitter datasets that include both texts and images  which
support the theoretical hypothesis.

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