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 about how to use
Genetic Algorithms, Naive Bayes and Artificial Neural Networks for text
mining.
Table of Contents
Foreword
About the Author
Acknowledgements
CHAPTER I
Concepts of Text Mining
1) HISTORY of TEXT MINING
2) DEFINITION of TEXT MINING
3) COMPONENTS of TEXT MINING
4) PRACTICAL APPLICATIONS of TEXT MINING
CHAPTER II Text Mining Algorithms and Examples
1) INFORMATION RETRIEVAL
(i) Similarity
(ii) Vectorization
(iii) Calculating Term Weighting and Frequency
(iv) Measuring the quality of IR
2) INFORMATION EXTRACTION
(i) Lexical Analysis
(ii) Tokenization
(iii) Filtering: Stop-words
(iv) Lemmatization
(v) Bag of Words
(vi) N-Gram
(vii) Tagging/Annotation, XML
3) 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 Analysis
4) AUTOMATIC DOCUMENT SUMMARIZATION
(i) Extraction-based summarization
(ii) Abstraction-based summarization
(iii) Aided Summarization
CHAPTER III Text Mining With Python
1) STARTING A TEXT MINIG IMPLEMENTATION
2) PYTHON ENVIRONMENT
3) Examples with Python
Index
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