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Sentiment Analysis In Social Networks

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Sentiment Analysis in Social Networks

Sentiment Analysis in Social Networks Book
Author : Federico Alberto Pozzi,Elisabetta Fersini,Enza Messina,Bing Liu
Publisher : Morgan Kaufmann
Release : 2016-10-06
ISBN : 0128044381
Language : En, Es, Fr & De

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Book Description :

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 the UAE Social Networks Context

Sentiment Analysis in the UAE Social Networks Context Book
Author : Hind Yousif Obaid Sahoo
Publisher : Unknown
Release : 2017
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

The main goal of this study is to evaluate the effectiveness of automated opinion and sentiment analysis in social media in the UAE's telecommunications sector context. The study also investigates the possible impact of sentiments expressed in reviews and opinions published in social media on customer decisions to use a mobile Internet package (Etisalat data plan). More specifically, the study examines the relationship between emotions and the following four important customer outcomes: satisfaction, trust, loyalty, and intention to recommend or repurchase a product or service in the UAE telecommunications industry context. Unlike many other studies involving sentiment analysis in social media, this study is conducted in a non-western context: the United Arab Emirates (UAE). A Web-based questionnaire is used to obtain responses from a sample of 97 Etisalat data plan subscribers working in a different organizations in the UAE. The data are subjected to a range of analytical methods, such as analysis of descriptive statistics, reliability testing, Pearson correlation analysis, automated sentiment analysis using various methods and dictionaries, and regression analysis. The findings reveal that customer loyalty, trust, product satisfaction and intention to repurchase or recommend are linked positively and significantly with customer emotions. Emotions are found to positively affect customer trust towards a company, as well as ascertain their loyalty towards the brand. In addition, emotions are found to influence product satisfaction. Emotions also impact the customer's intention to recommend or repurchase the product. This study is the first empirical investigation of the efficacy of automated sentiment analysis of social media in the UAE telecommunications sector. The results show that sentiments expressed in customer reviews have a significant positive relationship with customers' emotions, and through these emotions, impact satisfaction, trust, loyalty and purchase recommendation in relation to product and services. Thus, companies in the UAE should invest in automated monitoring of sentiments and opinions in the social media and develop tactics and strategies for responding to the detected trends and opinions.

Sentiment Analysis of Textual Content in Social Networks

Sentiment Analysis of Textual Content in Social Networks Book
Author : Mohammed Hamood Abdullah Jabreel
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d'anàlisi i en diferents idiomes.Comencem proposant un sistema d'anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d'atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d'anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d'anàlisi de sentiments fins a determinar automàticament la intensitat d'una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l'anàlisi del sentiment depenent de l'objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l'anglès, disposen d'una àmplia gamma de recursos per permetre l'anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l'ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d'explotar tant les opinions com les seves preferències en el procés de classificació d'alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d'última generació i mostren troballes apassionants.

Sentiment Analysis for Social Media

Sentiment Analysis for Social Media Book
Author : Carlos A. Iglesias,Antonio Moreno
Publisher : MDPI
Release : 2020-04-02
ISBN : 3039285726
Language : En, Es, Fr & De

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Book Description :

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.

People Sentiment and Social Network Analytics with Excel

People  Sentiment and Social Network Analytics with Excel Book
Author : Mong Shen Ng
Publisher : Unknown
Release : 2019-06-23
ISBN : 9781075419515
Language : En, Es, Fr & De

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Book Description :

A lot of organizational data is often untapped unstructured data in the form of text & numbers. This is the only book that teaches you how to use Excel & Word for People Analytics, Text Analytics, Sentiment Analysis & Social Network Analysis with step-by-step print-screen instructions: 1) Text Analytics (Text Mining): Mine employee's resume, engagement surveys & Glassdoor comments to uncover insights, then visualize the comments using "Pro word cloud", a free Microsoft Word add-In. 2) Sentiment Analysis: Mine text from social network posts & Glassdoor comments, then run Sentiment Analysis using "Azure Machine", a free Excel add-In. Learn how to predict a company's average employee attrition rate. E.g. a company's average employee attrition rate is predicted to be 8.1%, if unemployment rate is 3.3%, GDP growth is 2.3% & its Glassdoor public sentiment rating is 5. 3) Social Network Analysis (SNA) & Organizational Network Analysis (ONA): Run SNA & ONA using "NodeXL", a free open-source Excel network analysis tool. Learn how to convert an employee's social network into a score, & then predict their performance rating. E.g. an employee is predicted to get a performance rating of "7", if their "Social Network Size" is 16, "Social Network Diversity Index" is 3.1 & "Skillsets Score" is 8. 4) Predictive People Analytics: Use Excel's Statistical Analysis tools (Decision trees, Correlation, Multiple & Logistic Regression) to run Predictive People Analytics covering: Employee Engagement, Employee Attrition & Absenteeism, Performance, Compensation & Benefits, Training & Development, Health, Safety & Environment, Diversity & Inclusion. For example, an employee is predicted to have a 60% probability of getting into accidents, if he is age 30, worked 2 years in the company, and took 6 days sick leave. An employee is predicted to get rated "7" for Customer Service, if the training program that they attended has a training evaluation score of "8".

Advances in Social Networking based Learning

Advances in Social Networking based Learning Book
Author : Christos Troussas,Maria Virvou
Publisher : Springer Nature
Release : 2020-01-20
ISBN : 3030391302
Language : En, Es, Fr & De

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Book Description :

This book discusses three important, hot research issues: social networking-based learning, machine learning-based user modeling and sentiment analysis. Although these three technologies have been widely used by researchers around the globe by academic disciplines and by R&D departments in the IT industry, they have not yet been used extensively for the purposes of education. The authors present a novel approach that uses adaptive hypermedia in e-learning models to personalize educational content and learning resources based on the needs and preferences of individual learners. According to reports, in 2018 the vast majority of internet users worldwide are active on social networks, and the global average social network penetration rate as of 2018 is close to half the population. Employing social networking technologies in the field of education allows the latest technological advances to be used to create interactive educational environments where students can learn, collaborate with peers and communicate with tutors while benefiting from a social and pedagogical structure similar to a real class. The book first discusses in detail the current trend of social networking-based learning. It then provides a novel framework that moves further away from digital learning technologies while incorporating a wide range of recent advances to provide solutions to future challenges. This approach incorporates machine learning to the student-modeling component, which also uses conceptual frameworks and pedagogical theories in order to further promote individualization and adaptivity in e-learning environments. Moreover, it examines error diagnosis, misconceptions, tailored testing and collaboration between students are examined and proposes new approaches for these modules. Sentiment analysis is also incorporated into the general framework, supporting personalized learning by considering the user’s emotional state, and creating a user-friendly learning environment tailored to students’ needs. Support for students, in the form of motivation, completes the framework. This book helps researchers in the field of knowledge-based software engineering to build more sophisticated personalized educational software, while retaining a high level of adaptivity and user-friendliness within human–computer interactions. Furthermore, it is a valuable resource for educators and software developers designing and implementing intelligent tutoring systems and adaptive educational hypermedia systems.

Social Media Mining with R

Social Media Mining with R Book
Author : Nathan Danneman,Richard Heimann
Publisher : Packt Publishing Ltd
Release : 2014-03-25
ISBN : 1783281782
Language : En, Es, Fr & De

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Book Description :

A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world. Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.

Learning Social Media Analytics with R

Learning Social Media Analytics with R Book
Author : Raghav Bali,Dipanjan Sarkar,Tushar Sharma
Publisher : Packt Publishing Ltd
Release : 2017-05-26
ISBN : 1787125467
Language : En, Es, Fr & De

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Book Description :

Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

Social Network Analytics for Contemporary Business Organizations

Social Network Analytics for Contemporary Business Organizations Book
Author : Bansal, Himani,Shrivastava, Gulshan,Nguyen, Gia Nhu,Stanciu, Loredana-Mihaela
Publisher : IGI Global
Release : 2018-03-23
ISBN : 1522550984
Language : En, Es, Fr & De

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Book Description :

Social technology is quickly becoming a vital tool in our personal, educational, and professional lives. Its use must be further examined in order to determine the role of social media technology in organizational settings to promote business development and growth. Social Network Analytics for Contemporary Business Organizations is a critical scholarly resource that analyzes the application of social media in business applications. Featuring coverage on a broad range of topics, such as business management, dynamic networks, and online interaction, this book is geared towards professionals, researchers, academics, students, managers, and practitioners actively involved in the business industry.

Encyclopedia of Social Network Analysis and Mining

Encyclopedia of Social Network Analysis and Mining Book
Author : Reda Alhajj,Jon Rokne
Publisher : Springer
Release : 2018-05-02
ISBN : 9781493971305
Language : En, Es, Fr & De

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Book Description :

Social Network Analysis and Mining Encyclopedia (ESNAM) is the first major reference work to integrate fundamental concepts and research directions in the areas of social networks and applications to data mining. The second edition of ESNAM is a truly outstanding reference appealing to researchers, practitioners, instructors and students (both undergraduate and graduate), as well as the general public. This updated reference integrates all basics concepts and research efforts under one umbrella. Coverage has been expanded to include new emerging topics such as crowdsourcing, opinion mining, and sentiment analysis. Revised content of existing material keeps the encyclopedia current. The second edition is intended for college students as well as public and academic libraries. It is anticipated to continue to stimulate more awareness of social network applications and research efforts. The advent of electronic communication, and in particul ar on-line communities, have created social networks of hitherto unimaginable sizes. Reflecting the interdisciplinary nature of this unique field, the essential contributions of diverse disciplines, from computer science, mathematics, and statistics to sociology and behavioral science, are described among the 300 authoritative yet highly readable entries. Students will find a world of information and insight behind the familiar façade of the social networks in which they participate. Researchers and practitioners will benefit from a comprehensive perspective on the methodologies for analysis of constructed networks, and the data mining and machine learning techniques that have proved attractive for sophisticated knowledge discovery in complex applications. Also addressed is the application of social network methodologies to other domains, such as web networks and biological networks.

Sentiment Analysis and Opinion Mining

Sentiment Analysis and Opinion Mining Book
Author : Bing Liu
Publisher : Morgan & Claypool Publishers
Release : 2012
ISBN : 1608458849
Language : En, Es, Fr & De

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Book Description :

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis.Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations.This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography

Python Social Media Analytics

Python Social Media Analytics Book
Author : Siddhartha Chatterjee,Michal Krystyanczuk
Publisher : Packt Publishing Ltd
Release : 2017-07-28
ISBN : 1787126757
Language : En, Es, Fr & De

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Book Description :

Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more Analyze and extract actionable insights from your social data using various Python tools A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn Understand the basics of social media mining Use PyMongo to clean, store, and access data in MongoDB Understand user reactions and emotion detection on Facebook Perform Twitter sentiment analysis and entity recognition using Python Analyze video and campaign performance on YouTube Mine popular trends on GitHub and predict the next big technology Extract conversational topics on public internet forums Analyze user interests on Pinterest Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes. Style and approach This book follows a step-by-step approach to teach readers the concepts of social media analytics using the Python programming language. To explain various data analysis processes, real-world datasets are used wherever required.

Computational Data and Social Networks

Computational Data and Social Networks Book
Author : Andrea Tagarelli,Hanghang Tong
Publisher : Springer Nature
Release : 2019-11-11
ISBN : 3030349802
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 8th International Conference on Computational Data and Social Networks, CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019. The 22 full and 8 short papers presented in this book were carefully reviewed and selected from 120 submissions. The papers appear under the following topical headings: Combinatorial Optimization and Learning; Influence Modeling, Propagation, and Maximization; NLP and Affective Computing; Computational Methods for Social Good; and User Profiling and Behavior Modeling.

Strategic Uses of Social Media for Improved Customer Retention

Strategic Uses of Social Media for Improved Customer Retention Book
Author : Al-Rabayah, Wafaa,Khasawneh, Rawan,Abu-shamaa, Rasha,Alsmadi, Izzat
Publisher : IGI Global
Release : 2016-11-09
ISBN : 1522516875
Language : En, Es, Fr & De

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Book Description :

Social networking venues have increased significantly in popularity in recent years. When utilized properly, these networks can offer many advantages within business contexts. Strategic Uses of Social Media for Improved Customer Retention is a pivotal reference source for the latest scholarly research on the implementation of online social networks in modern businesses and examines how such networks allow for a better understanding of clients and customers. Highlighting theoretical concepts, empirical case studies, and critical analyses, this book is ideally designed for researchers, practitioners, professionals, and upper-level students interested in improving and maintaining customer relationships.

Information Retrieval and Social Media Mining

Information Retrieval and Social Media Mining Book
Author : María N. Moreno García
Publisher : MDPI
Release : 2021-03-09
ISBN : 3036502467
Language : En, Es, Fr & De

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Book Description :

This book presents diverse contributions related to some of the latest advances in the field of personalization and recommender systems, as well as social media and sentiment analysis. The work comprises several articles that address different problems in these areas by means of recent techniques such as deep learning, methods to analyze the structure and the dynamics of social networks, and modern language processing approaches for sentiment analysis, among others. The proposals included in the book are representative of some highly topical research directions and cover different application domains where they have been validated. These go from the recommendation of hotels, movies, music, documents, or pharmacy cross-selling to sentiment analysis in the field of telemedicine and opinion mining on news, also including the study of social capital on social media and dynamics aspects of the Twitter social network.

Big Data Research for Social Sciences and Social Impact

Big Data Research for Social Sciences and Social Impact Book
Author : Miltiadis D. Lytras,Anna Visvizi,Kwok Tai Chui
Publisher : MDPI
Release : 2020-03-19
ISBN : 3039282204
Language : En, Es, Fr & De

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Book Description :

A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.

SENTIMENT ANALYSIS OF ENGLISH TWEETS USING DATA MINING

SENTIMENT ANALYSIS OF ENGLISH TWEETS USING DATA MINING Book
Author : Dr. Gaurav Gupta,Dr. Gurjit Singh Bhathal
Publisher : BookRix
Release : 2018-03-26
ISBN : 3743852535
Language : En, Es, Fr & De

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Book Description :

Due to the popularity of internet it becomes very easy for people to share their views over social networking websites. Most popular website among them is twitter. Twitter is a widely used social networking website that is used by the numerous people to give their opinion regarding a particular topic or product. So, today it becomes necessary to analyze the tweet of the people. The process to analyze and interpret the tweets is known as sentiment analysis. The main motive of this project is to identify how the tweets on the social networking website are used to identify the opinion of people regarding the particular product or policy. Twitter is a online website that allows the user to post the status of maximum 140 characters. Twitter has over 200 million registered users and 100 million active users [34]. So it comes to be a great source of valuable information. This project aims to develop a better way for sentiment analysis which is nothing a simple way to classify the tweets into positive, negative or neutral. The result of the sentiment analysis can be used by various organizations. Sentiment analysis can be used for forecasting the stock exchange, used to predict the popularity of any product in market, or used to predict the result of elections based on the public views on the social sites. The main motive of project is to develop a better way to accurately classify the unknown tweets according to their content.

Social Networking and Computational Intelligence

Social Networking and Computational Intelligence Book
Author : Rajesh Kumar Shukla,Jitendra Agrawal,Sanjeev Sharma,Narendra S. Chaudhari,K. K. Shukla
Publisher : Springer Nature
Release : 2020-03-21
ISBN : 9811520712
Language : En, Es, Fr & De

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Book Description :

This book presents a selection of revised and extended versions of the best papers from the First International Conference on Social Networking and Computational Intelligence (SCI-2018), held in Bhopal, India, from October 5 to 6, 2018. It discusses recent advances in scientific developments and applications in these areas.

Social Media Processing

Social Media Processing Book
Author : Xueqi Cheng,Weiying Ma,Huan Liu,Huawei Shen,Shizheng Feng,Xing Xie
Publisher : Springer
Release : 2017-10-24
ISBN : 9811068054
Language : En, Es, Fr & De

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Book Description :

This book constitutes the thoroughly refereed proceedings of the 6th National Conference of Social Media Processing, SMP 2017, held in Beijing, China, in September 2017. The 28 revised full papers presented were carefully reviewed and selected from 140 submissions. The papers address issues such as: knowledge discovery for data; natural language processing; text mining and sentiment analysis; social network analysis and social computing.

Social Computing and Social Media Applications and Analytics

Social Computing and Social Media  Applications and Analytics Book
Author : Gabriele Meiselwitz
Publisher : Springer
Release : 2017-06-28
ISBN : 3319585622
Language : En, Es, Fr & De

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Book Description :

This book constitutes the proceedings of the 9th International Conference on Social Computing and Social Media, SCSM 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCII 2017, held in Vancouver, Canada, in July 2017. HCII 2017 received a total of 4340 submissions, of which 1228 papers were accepted for publication after a careful reviewing process. The papers thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The two volumes set of SCSM 2017 presents 67 papers which are organized in the following topical sections: user experience and behavior in social media, costumer behavior and social media, social issues in social media, social media for communication, learning and aging, opinion mining and sentiment analysis, social data and analytics.