Text Mining In Python
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Applied Text Mining
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, includingmodels 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.
Concepts of Text Mining
This book presents the concepts, implementation of text mining with real life examples implemented using Python libraries.You will find ideas how to use texts for extracting valuable and applicable information. The book is designed for academicians, students, researchers and those who are working as data scientist in sector.The book not only defines but also gives Python examples of Information Retrieval, Information Extraction, Concept Extraction, Classification, Clustering, Sentiment Analysis, Topic Extraction, Text Summarization, Web Mining. In the book you will also find a practical example how to use Genetic Algorithms, Naive Bayes and Artificial Neural Networks for text mining.Table of ContentsForewordAbout the AuthorAcknowledgementsCHAPTER I Concepts of Text Mining1)HISTORY of TEXT MINING2)DEFINITION of TEXT MINING3)COMPONENTS of TEXT MINING4. PRACTICAL APPLICATIONS of TEXT MININGCHAPTER II Text Mining Algorithms and Examples1)INFORMATION RETRIEVAL(i) Similarity:(ii) Vectorization:(iii) Calculating Term Weighting and Frequency(iv) Measuring the quality of IR2)INFORMATION EXTRACTION(i) Lexical Analysis(ii) Tokenization(iii) Filtering: Stop-words(iv) Lemmatization(v) Bag of Words(vi) N-Gram(vii) Tagging/Annotation, XML3)BASIC TASKS FOR TEXT MINING(i)Text Categorization(ii)Data Mining Techniques: Link And Association Analysis, Visualization, And Predictive Analytics(iii)Pattern Recognition(iv)Text Clustering And Word Clouding(v)Natural Language Processing (NLP) (vi) Sentiment Analysis4)AUTOMATIC DOCUMENT SUMMARIZATION(i)Extraction-based summarization(ii) Abstraction-based summarization(iii) Aided SummarizationCHAPTER III Text Mining With Python1) STARTING A TEXT MINIG IMPLEMENTATION2) PYTHON ENVIRONMENT3) Examples with Python
Text Analysis with Python: A Research Oriented Guide
Author: Mamta Mittal
language: en
Publisher: Bentham Science Publishers
Release Date: 2022-08-12
Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Introduces the reader to the preliminaries of natural language processing (NLP) · Covers data analysis and visualization using predefined python libraries and datasets · Teaches how to write text mining programs in Python · Includes text classification and clustering techniques · Informs the reader about different types of neural networks for text analysis · Includes advanced analytical techniques such as fuzzy logic and deep learning techniques · Explains concepts in a simplified and structured way that is ideal for learners · Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.