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Author: R.Pallavi Reddy
Publisher: Archers & Elevators Publishing House
Published:
Total Pages: 89
ISBN-13: 8119385411
DOWNLOAD EBOOKAuthor: Emil Hvitfeldt
Publisher: CRC Press
Published: 2021-10-22
Total Pages: 369
ISBN-13: 1000461998
DOWNLOAD EBOOKText data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Author: Jan Žižka
Publisher: CRC Press
Published: 2019-10-31
Total Pages: 327
ISBN-13: 0429890265
DOWNLOAD EBOOKThis book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.
Author: Sholom M. Weiss
Publisher: Springer
Published: 2015-09-07
Total Pages: 249
ISBN-13: 1447167503
DOWNLOAD EBOOKThis 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.
Author: Mandar Shivaji Hanchate
Publisher:
Published: 2023
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKIn recent times, Email and text messages are widely used to communicate as the number of cell phones/mobiles has increased drastically. Short Message Service (SMS) is one of the best and fast ways to communicate. SMSs are used and sent globally for personal and business purposes. But along with important SMSs, we receive other unimportant and fraudulent SMSs too, which is very inconvenient to the users. A lot of bogus messages are being sent for both personal and professional reasons, which is contributing to the problem of SMS spam. Accurately identifying spam SMS is a difficult and important endeavor and the detection of spam is seen as a serious issue in text analysis. The objective of this research is to build a model utilizing machine learning and deep learning principles so that we can understand the semantics of text and then categorize the SMSs as precisely as possible in the spam or non-spam/ham/legitimate classes. Here we used a pre-trained BERT model and collaborated it with several machine learning and deep learning model, among these models, BERT+SVC and BERT+BiLSTM performed the best with 99.10% and 99.19% accuracy respectively on the test dataset.
Author: Usman Qamar
Publisher: Springer Nature
Published: 2024
Total Pages: 505
ISBN-13: 3031519175
DOWNLOAD EBOOKThis 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.
Author: Murugan Anandarajan
Publisher: Springer
Published: 2018-10-19
Total Pages: 294
ISBN-13: 3319956639
DOWNLOAD EBOOKThis book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.
Author: Michael W. Berry
Publisher: John Wiley & Sons
Published: 2010-05-03
Total Pages: 229
ISBN-13: 0470749822
DOWNLOAD EBOOKText Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when “words are not enough.” This book: Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Presents a survey of text visualization techniques and looks at the multilingual text classification problem. Discusses the issue of cybercrime associated with chatrooms. Features advances in visual analytics and machine learning along with illustrative examples. Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.
Author: Domenica Fioredistella Iezzi
Publisher: Springer Nature
Published: 2020-11-24
Total Pages: 298
ISBN-13: 3030526801
DOWNLOAD EBOOKFocusing on methodologies, applications and challenges of textual data analysis and related fields, this book gathers selected and peer-reviewed contributions presented at the 14th International Conference on Statistical Analysis of Textual Data (JADT 2018), held in Rome, Italy, on June 12-15, 2018. Statistical analysis of textual data is a multidisciplinary field of research that has been mainly fostered by statistics, linguistics, mathematics and computer science. The respective sections of the book focus on techniques, methods and models for text analytics, dictionaries and specific languages, multilingual text analysis, and the applications of text analytics. The interdisciplinary contributions cover topics including text mining, text analytics, network text analysis, information extraction, sentiment analysis, web mining, social media analysis, corpus and quantitative linguistics, statistical and computational methods, and textual data in sociology, psychology, politics, law and marketing.
Author: Giuseppe Carenini
Publisher: Morgan & Claypool Publishers
Published: 2011
Total Pages: 133
ISBN-13: 1608453901
DOWNLOAD EBOOKThis book presents a set of computational methods to extract information from conversational data (e.g., meeting transcripts and emails) and to provide natural language summaries of the data. Very recent approaches for dealing with blogs, discussion forums, texts, and microblogs (e.g., Twitter) are also discussed. --Derived from book cover.
