Sentiment Analysis in Social Networks

Sentiment Analysis in Social Networks
Author: Federico Alberto Pozzi
Publisher: Morgan Kaufmann
Total Pages: 286
Release: 2016-10-06
Genre: Computers
ISBN: 0128044381

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The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network mining Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics

Sentiment Analysis in Social Networks

Sentiment Analysis in Social Networks
Author: Federico Pozzi
Publisher: Morgan Kaufmann Publishers
Total Pages: 242
Release: 2016-10-01
Genre:
ISBN: 9780128044124

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The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking." Sentiment Analysis in Social Networks" begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologiesProvides insights into opinion spamming, reasoning, and social network analysisShows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologiesProvides insights into opinion spamming, reasoning, and social network miningShows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one-stop reference for the state-of-the-art in social media analytics

Social Big Data Analytics

Social Big Data Analytics
Author: Bilal Abu-Salih
Publisher: Springer Nature
Total Pages: 218
Release: 2021-03-10
Genre: Business & Economics
ISBN: 9813366524

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This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process. This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities. The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning. This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display.

Social Network Based Big Data Analysis and Applications

Social Network Based Big Data Analysis and Applications
Author: Mehmet Kaya
Publisher: Springer
Total Pages: 254
Release: 2018-05-10
Genre: Social Science
ISBN: 3319781960

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This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2016), which was held in August 2016. The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.

Social Media Analytics for User Behavior Modeling

Social Media Analytics for User Behavior Modeling
Author: Arun Reddy Nelakurthi
Publisher: CRC Press
Total Pages: 81
Release: 2020-01-21
Genre: Computers
ISBN: 1000025403

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In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.

Social Network Analytics

Social Network Analytics
Author: Nilanjan Dey
Publisher: Academic Press
Total Pages: 267
Release: 2018-11-16
Genre: Computers
ISBN: 0128156414

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Social Network Analytics: Computational Research Methods and Techniques focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging area of research. In addition, it includes a variety of applications from several domains, such as scientific research, and the business and industrial sectors. The technical aspects of analysis are covered in detail, including visualizing and modeling, network theory, mathematical models, the big data analytics of social networks, multidimensional scaling, and more. As analyzing social network data is rapidly gaining interest in the scientific research community because of the importance of the information and insights that can be culled from the wealth of data inherent in the various aspects of the network, this book provides insights on measuring the relationships and flows between people, groups, organizations, computers, URLs, and more. Examines a variety of data analytic techniques that can be applied to social networks Discusses various methods of visualizing, modeling and tracking network patterns, organization, growth and change Covers the most recent research on social network analysis and includes applications to a number of domains

Proceedings of 5th International Conference on Big Data Analysis and Data Mining 2018

Proceedings of 5th International Conference on Big Data Analysis and Data Mining 2018
Author: ConferenceSeries
Publisher: ConferenceSeries
Total Pages: 90
Release:
Genre:
ISBN:

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June 20-22, 2018 Rome, Italy Key Topics : Data Mining Applications in Science, Engineering, Healthcare and Medicine, Big Data in Nursing Research, Data Mining and Machine Learning, Big Data Analytics, Optimization and Big Data, Big data technologies, Big Data algorithm, Big Data Applications, Forecasting from Big Data, Data Mining Methods and Algorithms, Artificial Intelligence, Data privacy and ethics, Data Warehousing, Data Mining Tools and Software, Data Mining Tasks and Processes, Data Mining analysis, Cloud computing, Internet of things (IOT), Social network analysis, Complexity and algorithms, Business Analytics, Open data, New visualization techniques, Search and data mining, Frequent pattern mining, Clustering, Others

Integrated Real-time Social Media Sentiment Analysis Service Using a Big Data Analytic Ecosystem

Integrated Real-time Social Media Sentiment Analysis Service Using a Big Data Analytic Ecosystem
Author: Danielle C. Aring
Publisher:
Total Pages:
Release: 2017
Genre:
ISBN:

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Big data analytics are at the center of modern science and business. Our social media networks, mobile devices and enterprise systems generate enormous volumes of it on a daily basis. This wide range of availability provides many organizations in every field opportunities to discover valuable intelligence for critical decision-making. However, traditional analytic architectures are insufficient to handle unprecedentedly big volume of data and complexity of data processing.This thesis presents an analytic framework to combat unprecedented scale of big data that performs data stream sentiment analysis effectively in real time. The work presents a Social Media Big Data Sentiment Analytics Service System (SMBDSASS). The architecture leverages Apache Spark stream data processing framework, coupled with a NoSQL Hive big data ecosystem. Two sentiment analysis models were developed; the first, a topic based model, given user provided topic or person of interest sentiment (opinion) analysis was performed on related topic sentences in a tweet stream. The second, an aspect (feature) based model given user provided product of interest and related product features aspect (feature) analysis was performed on reviews containing important feature terms.The experimental results of the proposed framework using real time tweet stream and product reviews show comparable improvements from the results of the existing literature, with 73% accuracy for topic-based sentiment model, and 74% accuracy for aspect (feature) based sentiment model.The work demonstrated that our topic and aspect based sentiment analysis models on the real time stream data processing framework using Apache Spark and machine learning classifiers coupled with a NoSQL big data ecosystem offer an efficient, scalable, real-time stream data-processing alternative for the complex multiphase sentiment analysis over common batch data mining frameworks.

Big Data Analytics in Cognitive Social Media and Literary Texts

Big Data Analytics in Cognitive Social Media and Literary Texts
Author: Sanjiv Sharma
Publisher: Springer Nature
Total Pages: 316
Release: 2021-10-10
Genre: Language Arts & Disciplines
ISBN: 9811647291

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This book provides a comprehensive overview of the theory and praxis of Big Data Analytics and how these are used to extract cognition-related information from social media and literary texts. It presents analytics that transcends the borders of discipline-specific academic research and focuses on knowledge extraction, prediction, and decision-making in the context of individual, social, and national development. The content is divided into three main sections: the first of which discusses various approaches associated with Big Data Analytics, while the second addresses the security and privacy of big data in social media, and the last focuses on the literary text as the literary data in Big Data Analytics. Sharing valuable insights into the etiology behind human cognition and its reflection in social media and literary texts, the book benefits all those interested in analytics that can be applied to literature, history, philosophy, linguistics, literary theory, media & communication studies and computational/digital humanities.