Low Resource Social Media Text Mining

This book focuses on methods that are unsupervised or require minimal supervision—vital in the low-resource domain. Over the past few years, rapid growth in Internet access across the globe has resulted in an explosion in user-generated text content in social media platforms. This effect is significantly pronounced in linguistically diverse areas of the world like South Asia, where over 400 million people regularly access social media platforms. YouTube, Facebook, and Twitter report a monthly active user base in excess of 200 million from this region. Natural language processing (NLP) research and publicly available resources such as models and corpora prioritize Web content authored primarily by a Western user base. Such content is authored in English by a user base fluent in the language and can be processed by a broad range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions features high levels of multilinguality, code-switching, and varied language skill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text. This book is designed for NLP practitioners well versed in recent advances in the field but unfamiliar with the landscape of low-resource multilingual NLP. The contents of this book introduce the various challenges associated with social media content, quantify these issues, and provide solutions and intuition. When possible, the methods discussed are evaluated on real-world social media data sets to emphasize their robustness to the noisy nature of the social media environment. On completion of the book, the reader will be well-versed with the complexity of text-mining in multilingual, low-resource environments; will be aware of a broad set of off-the-shelf tools that can be applied to various problems; and will be able to conduct sophisticated analyses of such text.

This book focuses on methods that are unsupervised or require minimal supervision-vital in the low-resource domain. Over the past few years, rapid growth in Internet access across the globe has resulted in an explosion in user-generated text content in social media platforms. This effect is significantly pronounced in linguistically diverse areas of the world like South Asia, where over 400 million people regularly access social media platforms. YouTube, Facebook, and Twitter report a monthly active user base in excess of 200 million from this region. Natural language processing (NLP) research and publicly available resources such as models and corpora prioritize Web content authored primarily by a Western user base. Such content is authored in English by a user base fluent in the language and can be processed by a broad range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions features high levels of multilinguality, code-switching, and varied language skill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text. This book is designed for NLP practitioners well versed in recent advances in the field but unfamiliar with the landscape of low-resource multilingual NLP. The contents of this book introduce the various challenges associated with social media content, quantify these issues, and provide solutions and intuition. When possible, the methods discussed are evaluated on real-world social media data sets to emphasize their robustness to the noisy nature of the social media environment. On completion of the book, the reader will be well-versed with the complexity of text-mining in multilingual, low-resource environments; will be aware of a broad set of off-the-shelf tools that can be applied to various problems; and will be able to conduct sophisticated analyses of such text.

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

Students in social science courses communicate, socialize, shop, learn, and work online. When they are asked to collect data for course projects they are often drawn to social media platforms and other online sources of textual data. There are many software packages and programming languages available to help students collect data online, and there are many texts designed to help with different forms of online research, from surveys to ethnographic interviews. But there is no textbook available that teaches students how to construct a viable research project based on online sources of textual data such as newspaper archives, site user comment archives, digitized historical documents, or social media user comment archives. Gabe Ignatow and Rada F. Mihalcea's new text An Introduction to Text Mining will be a starting point for undergraduates and first-year graduate students interested in collecting and analyzing textual data from online sources, and will cover the most critical issues that students must take into consideration at all stages of their research projects, including: ethical and philosophical issues; issues related to research design; web scraping and crawling; strategic data selection; data sampling; use of specific text analysis methods; and report writing.

What Is Text Mining Text mining, also known as text data mining (TDM) or text analytics, is the technique of extracting useful information from text. Related terms include text data mining (TDM) and text analytics. It is "the discovery by computer of new, previously unknown information by automatically extracting information from various written resources," according to one definition of the term. Websites, books, emails, reviews, and articles are all examples of written materials that may be utilized. Typically, the best way to acquire high-quality information is to construct patterns and trends through the use of methods such as statistical pattern learning. According to Hotho et al. (2005), we are able to differentiate between three distinct perspectives of text mining. These perspectives are information extraction, data mining, and a process known as knowledge discovery in databases (KDD). Text mining often entails the process of structuring the text that is input, determining patterns within the data that has been structured, and then lastly evaluating and interpreting the result of the mining process. When discussing text mining, the term "high quality" typically relates to some combination of the concepts of relevance, novelty, and interest. Text categorization, text clustering, concept/entity extraction, generation of granular taxonomies, sentiment analysis, document summarizing, and entity relation modeling are all examples of typical text mining activities. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Text Mining Chapter 2: Natural Language Processing Chapter 3: Data Mining Chapter 4: Information Extraction Chapter 5: Semantic Similarity Chapter 6: Unstructured Data Chapter 7: Biomedical Text Mining Chapter 8: Sentiment Analysis Chapter 9: Word Embedding Chapter 10: Social Media Mining (II) Answering the public top questions about text mining. (III) Real world examples for the usage of text mining in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of text mining' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of text mining.

Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it. Text Mining brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections. This accessible book, written by a sociologist and a computer scientist, surveys the fast-changing landscape of data sources, programming languages, software packages, and methods of analysis available today. Suitable for novice and experienced researchers alike, the book will help readers use text mining techniques more efficiently and productively.

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining. Introduces text analysis and text mining tools Provides a comprehensive overview of costs and benefits Introduces the topic, making it accessible to a general audience in a variety of fields, including examples from biology, chemistry, sociology, and criminology

Gain a foundational understanding of the analysis of textual data sets from social media sites, digital archives, and digital surveys and interviews through the study of language and social interactions in digital environments. This course is perfect for social scientists who want to gain a conceptual overview of the text mining landscape to take first steps towards working on a text mining project or collaborating with computational colleagues. By taking this course you will: Learn the foundations of Natural Language Processing (NLP) Learn how text mining tools have been used successfully by social scientists Understand basic text processing techniques Understand how to approach narrative analysis, thematic analysis, and metaphor analysis Learn about key computer science methods for text mining, such as text classification and opinion mining.