Comparison Of Neutrosophic Approach To Various Deep Learning Models For Sentiment Analysis

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Comparison of neutrosophic approach to various deep learning models for sentiment analysis

Author : Mayukh Sharma
Publisher : Infinite Study
ISBN 13 :
Total Pages : 14 pages
Book Rating : 4./5 ( download)

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Book Synopsis Comparison of neutrosophic approach to various deep learning models for sentiment analysis by : Mayukh Sharma

Download or read book Comparison of neutrosophic approach to various deep learning models for sentiment analysis written by Mayukh Sharma and published by Infinite Study. This book was released on with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has been widely used in numerous real-world engineering applications and for classification problems. Real-world data is present with neutrality and indeterminacy, which neutrosophic theory captures clearly. Though both are currently developing research areas, there has been little study on their interlinking. We have proposed a novel framework to implement neutrosophy in deep learning models. Instead of just predicting a single class as output, we have quantified the sentiments using three membership functions to understand them better. Our proposed model consists of two blocks, feature extraction, and feature classification.

Senti-NSetPSO: large-sized document-level sentiment analysis using Neutrosophic Set and particle swarm optimization

Author : Amita Jain
Publisher : Infinite Study
ISBN 13 :
Total Pages : 14 pages
Book Rating : 4./5 ( download)

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Book Synopsis Senti-NSetPSO: large-sized document-level sentiment analysis using Neutrosophic Set and particle swarm optimization by : Amita Jain

Download or read book Senti-NSetPSO: large-sized document-level sentiment analysis using Neutrosophic Set and particle swarm optimization written by Amita Jain and published by Infinite Study. This book was released on with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade, opinion mining has been explored by using various machine learning methods. In the literature, document-level sentiment analysis has been majorly dealt with short-sized text only. For large-sized text, document-level sentiment analysis has never been dealt. In this paper, a hybrid framework named as ‘‘Senti-NSetPSO’’ is proposed to analyse large-sized text. Senti-NSetPSO comprises of two classifiers: binary and ternary based on hybridization of particle swarm optimization (PSO) with Neutrosophic Set. This method is suitable to classify large-sized text having more than 25 kb of size. Swarm size generated from large text can give a suitable measurement for implementation of PSO convergence. The proposed approach is trained and tested for large-sized text collected from Blitzer, aclIMDb, Polarity and Subjective Dataset. The proposed method establishes a co-relation between sentiment analysis and Neutrosophic Set. On Blitzer, aclIMDb and Polarity dataset, the model acquires satisfactory accuracy by ternary classifier. The accuracy of ternary classifier of the proposed framework shows significant improvement than review paper classifier present in the literature.

Neutrosophic SuperHyperAlgebra and New Types of Topologies

Author : Florentin Smarandache
Publisher : Infinite Study
ISBN 13 :
Total Pages : 254 pages
Book Rating : 4./5 ( download)

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Book Synopsis Neutrosophic SuperHyperAlgebra and New Types of Topologies by : Florentin Smarandache

Download or read book Neutrosophic SuperHyperAlgebra and New Types of Topologies written by Florentin Smarandache and published by Infinite Study. This book was released on 2023-09-01 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: In general, a system S (that may be a company, association, institution, society, country, etc.) is formed by sub-systems Si { or P(S), the powerset of S }, and each sub-system Si is formed by sub-sub-systems Sij { or P(P(S)) = P2(S) } and so on. That’s why the n-th PowerSet of a Set S { defined recursively and denoted by Pn(S) = P(Pn-1(S) } was introduced, to better describes the organization of people, beings, objects etc. in our real world. The n-th PowerSet was used in defining the SuperHyperOperation, SuperHyperAxiom, and their corresponding Neutrosophic SuperHyperOperation, Neutrosophic SuperHyperAxiom in order to build the SuperHyperAlgebra and Neutrosophic SuperHyperAlgebra. In general, in any field of knowledge, one in fact encounters SuperHyperStructures. Also, six new types of topologies have been introduced in the last years (2019-2022), such as: Refined Neutrosophic Topology, Refined Neutrosophic Crisp Topology, NeutroTopology, AntiTopology, SuperHyperTopology, and Neutrosophic SuperHyperTopology.

Deep Learning-Based Approaches for Sentiment Analysis

Author : Basant Agarwal
Publisher : Springer Nature
ISBN 13 : 9811512167
Total Pages : 326 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Deep Learning-Based Approaches for Sentiment Analysis by : Basant Agarwal

Download or read book Deep Learning-Based Approaches for Sentiment Analysis written by Basant Agarwal and published by Springer Nature. This book was released on 2020-01-24 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neutrosophic speech recognition Algorithm for speech under stress by Machine learning

Author : D. Nagarajan
Publisher : Infinite Study
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4./5 ( download)

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Book Synopsis Neutrosophic speech recognition Algorithm for speech under stress by Machine learning by : D. Nagarajan

Download or read book Neutrosophic speech recognition Algorithm for speech under stress by Machine learning written by D. Nagarajan and published by Infinite Study. This book was released on 2023-01-01 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is well known that the unpredictable speech production brought on by stress from the task at hand has a significant negative impact on the performance of speech processing algorithms. Speech therapy benefits from being able to detect stress in speech. Speech processing performance suffers noticeably when perceptually produced stress causes variations in speech production. Using the acoustic speech signal to objectively characterize speaker stress is one method for assessing production variances brought on by stress. Real-world complexity and ambiguity make it difficult for decision-makers to express their conclusions with clarity in their speech. In particular, the Neutrosophic speech algorithm is used to encode the language variables because they cannot be computed directly. Neutrosophic sets are used to manage indeterminacy in a practical situation. Existing algorithms are used except for stress on Neutrosophic speech recognition. The creation of algorithms that calculate, categorize, or differentiate between different stress circumstances. Understanding stress and developing strategies to combat its effects on speech recognition and human-computer interaction system are the goals of this recognition.

Biologically Inspired Techniques in Many Criteria Decision Making

Author : Satchidananda Dehuri
Publisher : Springer Nature
ISBN 13 : 9811687390
Total Pages : 718 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Biologically Inspired Techniques in Many Criteria Decision Making by : Satchidananda Dehuri

Download or read book Biologically Inspired Techniques in Many Criteria Decision Making written by Satchidananda Dehuri and published by Springer Nature. This book was released on 2022-06-03 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes best-selected, high-quality research papers presented at Second International Conference on Biologically Inspired Techniques in Many Criteria Decision Making (BITMDM 2021) organized by Department of Information & Communication Technology, Fakir Mohan University, Balasore, Odisha, India, during December 20-21, 2021. This proceeding presents the recent advances in techniques which are biologically inspired and their usage in the field of many criteria decision making. The topics covered are biologically inspired algorithms, nature-inspired algorithms, multi-criteria optimization, multi-criteria decision making, data mining, big-data analysis, cloud computing, IOT, machine learning and soft computing, smart technologies, crypt-analysis, cognitive informatics, computational intelligence, artificial intelligence and machine learning, data management exploration and mining, computational intelligence, and signal and image processing.

Evolution in Signal Processing and Telecommunication Networks

Author : P. Satish Rama Chowdary
Publisher : Springer Nature
ISBN 13 : 9811685541
Total Pages : 525 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Evolution in Signal Processing and Telecommunication Networks by : P. Satish Rama Chowdary

Download or read book Evolution in Signal Processing and Telecommunication Networks written by P. Satish Rama Chowdary and published by Springer Nature. This book was released on 2022-03-23 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the latest developments and outlines future trends in the fields of microelectronics, electromagnetics and telecommunication. It contains original research works presented at the International Conference on Microelectronics, Electromagnetics and Telecommunication (ICMEET 2021), held in Bhubaneswar, Odisha, India during 27–28 August, 2021. The papers were written by scientists, research scholars and practitioners from leading universities, engineering colleges and R&D institutes from all over the world and share the latest breakthroughs in and promising solutions to the most important issues facing today’s society.

Sentiment Analysis and Deep Learning

Author : Subarna Shakya
Publisher : Springer Nature
ISBN 13 : 9811954437
Total Pages : 987 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Sentiment Analysis and Deep Learning by : Subarna Shakya

Download or read book Sentiment Analysis and Deep Learning written by Subarna Shakya and published by Springer Nature. This book was released on 2023-01-01 with total page 987 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks

Author : Arindam Chaudhuri
Publisher :
ISBN 13 : 9789811374753
Total Pages : pages
Book Rating : 4.3/5 (747 download)

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Book Synopsis Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks by : Arindam Chaudhuri

Download or read book Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks written by Arindam Chaudhuri and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. --

Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing

Author : Gyanendra Verma
Publisher : Bentham Science Publishers
ISBN 13 : 9815079220
Total Pages : 270 pages
Book Rating : 4.8/5 (15 download)

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Book Synopsis Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing by : Gyanendra Verma

Download or read book Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing written by Gyanendra Verma and published by Bentham Science Publishers. This book was released on 2023-08-21 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis

Author : Kritika Mishra
Publisher : Infinite Study
ISBN 13 :
Total Pages : 22 pages
Book Rating : 4./5 ( download)

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Book Synopsis A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis by : Kritika Mishra

Download or read book A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis written by Kritika Mishra and published by Infinite Study. This book was released on 2020-10-18 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.

Machine learning in Neutrosophic Environment: A Survey

Author : Azeddine Elhassouny
Publisher : Infinite Study
ISBN 13 :
Total Pages : 11 pages
Book Rating : 4./5 ( download)

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Book Synopsis Machine learning in Neutrosophic Environment: A Survey by : Azeddine Elhassouny

Download or read book Machine learning in Neutrosophic Environment: A Survey written by Azeddine Elhassouny and published by Infinite Study. This book was released on with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: Veracity in big data analytics is recognized as a complex issue in data preparation process, involving imperfection, imprecision and inconsistency. Single-valued Neutrosophic numbers (SVNs), have prodded a strong capacity to model such complex information. Many Data mining and big data techniques have been proposed to deal with these kind of dirty data in preprocessing stage. However, only few studies treat the imprecise and inconsistent information inherent in the modeling stage. However, this paper summarizes all works done about mapping machine learning algorithms from crisp number space to Neutrosophic environment. We discuss also contributions and hybridization of machine learning algorithms with Single-valued Neutrosophic numbers (SVNs) in modeling imperfect information, and then their impacts on resolving reel world problems. In addition, we identify new trends for future research, then we introduce, for the first time, a taxonomy of Neutrosophic learning algorithms, clarifying what algorithms are already processed or not, which makes it easier for domain researchers.

New Development of Neutrosophic Probability, Neutrosophic Statistics, Neutrosophic Algebraic Structures, and Neutrosophic Plithogenic Optimizations

Download New Development of Neutrosophic Probability, Neutrosophic Statistics, Neutrosophic Algebraic Structures, and Neutrosophic Plithogenic Optimizations PDF Online Free

Author : Florentin Smarandache
Publisher : Infinite Study
ISBN 13 :
Total Pages : 411 pages
Book Rating : 4./5 ( download)

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Book Synopsis New Development of Neutrosophic Probability, Neutrosophic Statistics, Neutrosophic Algebraic Structures, and Neutrosophic Plithogenic Optimizations by : Florentin Smarandache

Download or read book New Development of Neutrosophic Probability, Neutrosophic Statistics, Neutrosophic Algebraic Structures, and Neutrosophic Plithogenic Optimizations written by Florentin Smarandache and published by Infinite Study. This book was released on 2022-09-01 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents state-of-the-art papers on new topics related to neutrosophic theories, such as neutrosophic algebraic structures, neutrosophic triplet algebraic structures, neutrosophic extended triplet algebraic structures, neutrosophic algebraic hyperstructures, neutrosophic triplet algebraic hyperstructures, neutrosophic n-ary algebraic structures, neutrosophic n-ary algebraic hyperstructures, refined neutrosophic algebraic structures, refined neutrosophic algebraic hyperstructures, quadruple neutrosophic algebraic structures, refined quadruple neutrosophic algebraic structures, neutrosophic image processing, neutrosophic image classification, neutrosophic computer vision, neutrosophic machine learning, neutrosophic artificial intelligence, neutrosophic data analytics, neutrosophic deep learning, and neutrosophic symmetry, as well as their applications in the real world.

Deep Learning-based Approaches for Sentiment Analysis

Author :
Publisher :
ISBN 13 : 9789811512179
Total Pages : 326 pages
Book Rating : 4.5/5 (121 download)

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Book Synopsis Deep Learning-based Approaches for Sentiment Analysis by :

Download or read book Deep Learning-based Approaches for Sentiment Analysis written by and published by . This book was released on 2020 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Subarna Shakya
Publisher : Springer Nature
ISBN 13 : 9811651574
Total Pages : 1023 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Sentimental Analysis and Deep Learning by : Subarna Shakya

Download or read book Sentimental Analysis and Deep Learning written by Subarna Shakya and published by Springer Nature. This book was released on 2021-10-25 with total page 1023 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Prominent Feature Extraction for Sentiment Analysis

Author : Basant Agarwal
Publisher : Springer
ISBN 13 : 3319253433
Total Pages : 103 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Prominent Feature Extraction for Sentiment Analysis by : Basant Agarwal

Download or read book Prominent Feature Extraction for Sentiment Analysis written by Basant Agarwal and published by Springer. This book was released on 2015-12-14 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.

Deep Learning

Author : Gyanendra Verma
Publisher : Bentham Science Publishers
ISBN 13 : 9789815079234
Total Pages : 0 pages
Book Rating : 4.0/5 (792 download)

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Book Synopsis Deep Learning by : Gyanendra Verma

Download or read book Deep Learning written by Gyanendra Verma and published by Bentham Science Publishers. This book was released on 2023-08-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.