Sentimental Analysis And Deep Learning
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Deep Learning Based Approaches For Sentiment Analysis
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Author : Basant Agarwal
language : en
Publisher: Springer Nature
Release Date : 2020-01-24
Deep Learning Based Approaches For Sentiment Analysis written by Basant Agarwal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-24 with Technology & Engineering categories.
This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.
Sentimental Analysis And Deep Learning
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Author : Subarna Shakya
language : en
Publisher: Springer Nature
Release Date : 2021-10-25
Sentimental Analysis And Deep Learning written by Subarna Shakya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-25 with Technology & Engineering categories.
This book gathers selected papers presented at the International Conference on Sentimental Analysis and Deep Learning (ICSADL 2021), jointly organized by Tribhuvan University, Nepal; Prince of Songkla University, Thailand; and Ejesra during June, 18–19, 2021. The volume discusses state-of-the-art research works on incorporating artificial intelligence models like deep learning techniques for intelligent sentiment analysis applications. Emotions and sentiments are emerging as the most important human factors to understand the prominent user-generated semantics and perceptions from the humongous volume of user-generated data. In this scenario, sentiment analysis emerges as a significant breakthrough technology, which can automatically analyze the human emotions in the data-driven applications. Sentiment analysis gains the ability to sense the existing voluminous unstructured data and delivers a real-time analysis to efficiently automate the business processes. Meanwhile, deep learning emerges as the revolutionary paradigm with its extensive data-driven representation learning architectures. This book discusses all theoretical aspects of sentimental analysis, deep learning and related topics.
Sentiment Analysis And Deep Learning
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Author : Subarna Shakya
language : en
Publisher: Springer Nature
Release Date : 2023-01-01
Sentiment Analysis And Deep Learning written by Subarna Shakya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Technology & Engineering categories.
This book gathers selected papers presented at International Conference on Sentimental Analysis and Deep Learning (ICSADL 2022), jointly organized by Tribhuvan University, Nepal and Prince of Songkla University, Thailand during 16 – 17 June, 2022. The volume discusses state-of-the-art research works on incorporating artificial intelligence models like deep learning techniques for intelligent sentiment analysis applications. Emotions and sentiments are emerging as the most important human factors to understand the prominent user-generated semantics and perceptions from the humongous volume of user-generated data. In this scenario, sentiment analysis emerges as a significant breakthrough technology, which can automatically analyze the human emotions in the data-driven applications. Sentiment analysis gains the ability to sense the existing voluminous unstructured data and delivers a real-time analysis to efficiently automate the business processes.
Learning Tensorflow
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Author : Tom Hope
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-08-09
Learning Tensorflow written by Tom Hope and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-09 with Computers categories.
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting
Sentiment Analysis For Social Media
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Author : Carlos A. Iglesias
language : en
Publisher: MDPI
Release Date : 2020-04-02
Sentiment Analysis For Social Media written by Carlos A. Iglesias and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Technology & Engineering categories.
Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.
Handbook Of Research On Emerging Trends And Applications Of Machine Learning
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Author : Solanki, Arun
language : en
Publisher: IGI Global
Release Date : 2019-12-13
Handbook Of Research On Emerging Trends And Applications Of Machine Learning written by Solanki, Arun and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-13 with Computers categories.
As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.
Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks
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Author : Arindam Chaudhuri
language : en
Publisher: Springer
Release Date : 2019-04-06
Visual And Text Sentiment Analysis Through Hierarchical Deep Learning Networks written by Arindam Chaudhuri and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-06 with Computers categories.
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.
Natural Language Processing For Global And Local Business
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Author : Fatih Pinarbasi
language : en
Publisher:
Release Date : 2020
Natural Language Processing For Global And Local Business written by Fatih Pinarbasi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computational linguistics categories.
"This book explores the theoretical and practical phenomenon of natural language processing through different languages and platforms in terms of today's conditions"--
Sentiment Analysis With Machine Learning A Project Based Guide
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Author : Ashish Rajaram Lahase
language : en
Publisher: IIP Iterative International Publishers
Release Date : 2025-04-26
Sentiment Analysis With Machine Learning A Project Based Guide written by Ashish Rajaram Lahase and has been published by IIP Iterative International Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-26 with Computers categories.
In the beginning, we embark on a journey that invites reflection and contemplation. This introduction serves as a gateway to the ideas and themes that will unfold in the pages that follow. It is a moment to set the stage, to provide context, and to share the intentions behind this work. As we delve into the narrative, may the insights and experiences resonate deeply, guiding the reader through the exploration ahead. In recent years, the exploration of sentiment analysis has taken on significant importance within the realm of Natural Language Processing (NLP). This burgeoning field empowers businesses, researchers, and individuals alike to glean meaningful insights from the vast expanse of textual data. In an age where the proliferation of user-generated content on social media, product reviews, and online discussions is unprecedented, the comprehension of sentiments emerges as a crucial element for informed decision-making across diverse fields. This work, Sentiment Analysis with Machine Learning: A Project-Based Guide, aims to offer a thorough and pragmatic pathway for understanding and applying sentiment analysis techniques. This work has been thoughtfully organized to function as a practical resource, intertwining foundational theories with tangible applications in the real world. Our foremost intention is to guide readers in grasping the essential principles of sentiment analysis while simultaneously providing them with the opportunity to acquire hands-on experience through engaging in projects that utilize a range of machine learning techniques. In the following pages, we embark on a journey through essential themes, delving into the intricacies of text preprocessing, the art of feature engineering, the distinctions between supervised and unsupervised learning methods, the critical evaluation of sentiment analysis models. In this work, we present a selection of thoughtfully chosen project-based examples, designed to empower readers to translate their understanding into practical applications within real-world contexts. This work presents a meticulously organized, sequential methodology designed to serve the needs of students, researchers, and professionals aspiring to cultivate their proficiency in sentiment analysis. We wish to express our heartfelt appreciation to all those who have contributed to the creation of this work. From our esteemed mentors and dedicated colleagues to the vibrant research community that tirelessly propels this captivating field forward, your support has been invaluable. This work aspires to be a significant resource for individuals keen to delve into the possibilities of sentiment analysis within the dynamic realms of artificial intelligence and data science.