How Long to Read Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks

How Long Does it Take to Read Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks?

It takes the average reader 1 hour and 41 minutes to read Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks by Arindam Chaudhuri

Assuming a reading speed of 250 words per minute. Learn more

Description

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.

How long is Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks?

Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks by Arindam Chaudhuri is 98 pages long, and a total of 25,284 words.

This makes it 33% the length of the average book. It also has 31% more words than the average book.

How Long Does it Take to Read Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks Aloud?

The average oral reading speed is 183 words per minute. This means it takes 2 hours and 18 minutes to read Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks aloud.

What Reading Level is Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks?

Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks is suitable for students ages 10 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks?

Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks by Arindam Chaudhuri is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.

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