Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks

Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks

Chaudhuri, Arindam

Paperback

Series: Springerbriefs in Computer Science

DatabasesGeneral ComputersSystem Administration

ISBN10: 9811374732
ISBN13: 9789811374739
Publisher: Springer Nature
Published: Apr 15 2019
Pages: 98
Weight: 0.39
Height: 0.25 Width: 6.14 Depth: 9.21
Language: English

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.

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