Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks
暫譯: 透過階層深度學習網絡進行視覺與文本情感分析

Chaudhuri, Arindam

Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks
  • 出版商: Springer
  • 出版日期: 2019-04-15
  • 售價: $2,420
  • 貴賓價: 9.5 折 $2,299
  • 語言: 英文
  • 頁數: 98
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9811374732
  • ISBN-13: 9789811374739
  • 相關分類: DeepLearning

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商品描述

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.

商品描述(中文翻譯)

本書介紹了針對多模態情感分析的層次深度學習最新研究。此外，它利用層次門控反饋遞歸神經網絡（Hierarchical Gated Feedback Recurrent Neural Networks, HGFRNNs）分析Twitter部落格中的文本和視覺內容的情感。目前為止，已經進行了多項深度學習的研究，但大多數現有方法僅專注於文本內容或視覺內容。相對而言，所提出的情感分析模型可以應用於任何社交部落格數據集，使本書對於深度學習和情感分析的研究生和研究人員非常有益。

情感分析模型的數學抽象以非常清晰的方式呈現。通過結合文本和視覺預測結果來分析完整的情感。本書的創新之處在於開發了創新的層次遞歸神經網絡來分析情感；通過控制信號從上層遞歸層流向下層，堆疊多個遞歸層；評估不同類型的遞歸單元的HGFRNN；以及將HGFRNN層自適應分配到不同的時間尺度。考慮到需要利用大規模社交多媒體內容進行情感分析，本書使用了最先進的視覺和文本情感分析技術進行聯合視覺-文本情感分析。所提出的方法在包含文本和圖像的Twitter數據集上產生了有希望的結果，支持了理論假設。

作者簡介

Arindam Chaudhuri is currently working as Principal Data Scientist at the Samsung R & D Institute in Delhi, India. He has worked in industry, research, and academics in the domain of machine learning for the past 19 years. His current research interests include pattern recognition, machine learning, soft computing, optimization, and big data. He received his M.Tech and PhD in Computer Science from Jadavpur University, Kolkata, India and Netaji Subhas University, Kolkata, India in 2005 and 2011 respectively. He has published three research monographs and over 45 articles in international journals and conference proceedings.

作者簡介(中文翻譯)

阿林達姆·喬杜里目前擔任印度德里三星研發院的首席數據科學家。他在機器學習領域擁有19年的行業、研究和學術經驗。他目前的研究興趣包括模式識別、機器學習、軟計算、優化和大數據。他於2005年和2011年分別在印度加爾各答的賈達布爾大學和內塔吉·蘇巴斯大學獲得計算機科學的碩士和博士學位。他已發表三部研究專著以及超過45篇國際期刊和會議論文。

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