Mastering Social Media Mining with Python

Mastering Social Media Mining with Python
Author: Marco Bonzanini
Publisher: Packt Publishing Ltd
Total Pages: 333
Release: 2016-07-29
Genre: Computers
ISBN: 1783552026
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Acquire and analyze data from all corners of the social web with Python About This Book Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data This is your one-stop solution to fetching, storing, analyzing, and visualizing social media data Who This Book Is For This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data. What You Will Learn Interact with a social media platform via their public API with Python Store social data in a convenient format for data analysis Slice and dice social data using Python tools for data science Apply text analytics techniques to understand what people are talking about on social media Apply advanced statistical and analytical techniques to produce useful insights from data Build beautiful visualizations with web technologies to explore data and present data products In Detail Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data. Style and approach This practical, hands-on guide will help you learn everything you need to perform data mining for social media. Throughout the book, we take an example-oriented approach to use Python for data analysis and provide useful tips and tricks that you can use in day-to-day tasks.

Mastering Social Media Mining with R

Mastering Social Media Mining with R
Author: Sharan Kumar Ravindran
Publisher: Packt Publishing Ltd
Total Pages: 248
Release: 2015-09-23
Genre: Computers
ISBN: 1784399671
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Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.

Social Media Mining with R

Social Media Mining with R
Author: Richard Heimann
Publisher: Packt Pub Limited
Total Pages: 122
Release: 2014
Genre: Computers
ISBN: 9781783281770
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A concise, handson 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 handson 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.

Social Media Mining with R

Social Media Mining with R
Author: Nathan Danneman
Publisher:
Total Pages:
Release: 2014
Genre:
ISBN:
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Mastering Social Media Mining with R

Mastering Social Media Mining with R
Author: Vikram Garg
Publisher:
Total Pages: 248
Release: 2015-09-23
Genre: Computers
ISBN: 9781784396312
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Extract valuable data from your social media sites and make better business decisions using RAbout This Book• Explore the social media APIs in R to capture data and tame it• Employ the machine learning capabilities of R to gain optimal business value• A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media dataWho This Book Is ForIf you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful.What You Will Learn• Access APIs of popular social media sites and extract data• Perform sentiment analysis and identify trending topics• Measure CTR performance for social media campaigns• Implement exploratory data analysis and correlation analysis• Build a logistic regression model to detect spam messages• Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations• Develop recommendation systems using Collaborative Filtering and the Apriori algorithmIn DetailWith an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data.This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming.With this handy guide, you will be ready to embark on your journey as an independent social media analyst.Style and approachThis easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.

Mastering Text Mining with R

Mastering Text Mining with R
Author: Ashish Kumar
Publisher: Packt Publishing Ltd
Total Pages: 259
Release: 2016-12-28
Genre: Computers
ISBN: 1782174702
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Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.

Encyclopedia of Organizational Knowledge, Administration, and Technology

Encyclopedia of Organizational Knowledge, Administration, and Technology
Author: Khosrow-Pour D.B.A., Mehdi
Publisher: IGI Global
Total Pages: 2734
Release: 2020-09-29
Genre: Business & Economics
ISBN: 1799834743
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For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication.

R Data Science Essentials

R Data Science Essentials
Author: Raja B. Koushik
Publisher: Packt Publishing Ltd
Total Pages: 154
Release: 2016-01-13
Genre: Computers
ISBN: 1785286366
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Learn the essence of data science and visualization using R in no time at all About This Book Become a pro at making stunning visualizations and dashboards quickly and without hassle For better decision making in business, apply the R programming language with the help of useful statistical techniques. From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patterns Who This Book Is For If you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you. What You Will Learn Perform data preprocessing and basic operations on data Implement visual and non-visual implementation data exploration techniques Mine patterns from data using affinity and sequential analysis Use different clustering algorithms and visualize them Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm Extract patterns through visualization and build a forecasting algorithm Build a recommendation engine using different collaborative filtering algorithms Make a stunning visualization and dashboard using ggplot and R shiny In Detail With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision. Style and approach This easy-to-follow guide contains hands-on examples of the concepts of data science using R.

Applied Text Mining

Applied Text Mining
Author: Usman Qamar
Publisher: Springer Nature
Total Pages: 505
Release: 2024
Genre: Electronic books
ISBN: 3031519175
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This textbook covers the concepts, theories, and implementations of text mining and natural language processing (NLP). It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. It consists of three parts. In Part 1 which consists of three chapters details about basic concepts and applications of text mining are provided, including eg sentiment analysis and opinion mining. It builds a strong foundation for the reader in order to understand the remaining parts. In the five chapters of Part 2, all the core concepts of text analytics like feature engineering, text classification, text clustering, text summarization, topic mapping, and text visualization are covered. Finally, in Part 3 there are three chapters covering deep-learning-based text mining, which is the dominating method applied to practically all text mining tasks nowadays. Various deep learning approaches to text mining are covered, including models for processing and parsing text, for lexical analysis, and for machine translation. All three parts include large parts of Python code that shows the implementation of the described concepts and approaches. The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The implementation of every text mining task is carefully explained, based Python as the programming language and Spacy and NLTK as Natural Language Processing libraries. The book is suitable for both undergraduate and graduate students in computer science and engineering.

Enhancing Knowledge Discovery and Innovation in the Digital Era

Enhancing Knowledge Discovery and Innovation in the Digital Era
Author: Lytras, Miltiadis D.
Publisher: IGI Global
Total Pages: 383
Release: 2018-01-19
Genre: Education
ISBN: 1522541926
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With the dawn of electronic databases, information technologies, and the Internet, organizations, now more than ever, have easy access to all the knowledge they need to conduct their business. However, utilizing and detecting the beneficial information can pose as a challenge. Enhancing Knowledge Discovery and Innovation in the Digital Era is a vibrant reference source on the latest research on student education, open information, technology enhanced learning (TEL), and student outcomes. Featuring widespread coverage across a range of applicable perspectives and topics, such as engineering education, data mining, and 3D printing, this book is ideally designed for professionals, upper-level students, and academics seeking current research on knowledge management and innovation networks.