Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data (Paperback)

Chapter 1: Natural Language Basics.- Chapter Goal: Introduces the readers to the basics of NLP and Text processing No of pages: 40 - 50 Sub -Topics 1. Language Syntax and Structure 2. Text formats and grammars 3. Lexical and Text Corpora resources 4. Deep dive into the Wordnet corpus 5. Parts of speech, Stemming and lemmatization Chapter 2: Python Refresher for Text Analytics Chapter Goal: A useful chapter for people who do not know python as well as for experienced people who can use it as a quick reference for useful commands and techniques for text processing using python No of pages: 30 - 35 Sub - Topics 1. Python data structures and constructs 2. Functions, conditionals and code flow 3. Handling strings with Python 4. Regular Expressions with Python 5. Quick glance into nltk, gensim and pattern Chapter 3: Text Processing Chapter Goal: This chapter covers all the techniques and capabilities needed for processing and parsing text into easy to understand formats. We also look at how to segment and normalize text. No of pages : 35 - 40 Sub - Topics: 1. Sentence and word tokenization 2. Text tagging and chunking 3. Text Parse Trees 3. Text normalization 4. Text spell checks and removal of redundant characters 5. Synonyms and Synsets Chapter 4: Text Classification Chapter Goal: Introduces readers to the concept of classification as a supervised machine learning problem and looks at a real world example for classifying text documents No of pages: 40 - 45 Sub - Topics: 1. Classification basics 2. Types of classifiers 3. Feature generation of text documents 4. Types of feature generators 5. Building a text classifier on real world data 6. Evaluating Classifiers 7. Binary and multi-class classification models Chapter 5: Text summarization and topic modeling Chapter Goal: Introduces the concepts of text summarization, n-gram tagging analysis and topic models to the readers and looks at some real world datasets and hands-on implementations on the same No of pages: 40 - 45 Sub - Topics: 1. Text summarization concepts 2. Dimensionality reduction 3. N-gram tagging models 4. Topic modeling using LDA and LSA 5. Generate topics from real world data 6. N-gram analysis to generate patterns from app reviews Chapter 6: Text Clustering and Similarity analysis Chapter Goal: We look at unsupervised machine learning concepts here like text clustering and similarity measures No of pages: 35 - 40 Sub - Topics: 1. Clustering concepts 2. Analyzing text similarity 3. Implementing text similarity with cosine, jaccard measures 4. Text clustering algorithms 5. Hands on text clustering on real world data Chapter 7: Sentiment Analysis Chapter Goal: We look at solving a popular problem of analyzing sentiment from text using a combination of methods learnt earlier including classification and also lexical analysis No of pages: 35 - 40 Sub - Topics: 1. What is sentiment analysis 2. Looking at lexical corpora for sentiment 3. Analyzing sentiment using lexical analysis (hands-on) 4. Building a sentiment analysis classifier (hands-on)