Text Analytics with Python

3. Lexical and Text Corpora resources

4. Deep dive into the Wordnet corpus

5. Parts of speech, Stemming and lemmatization

Chapter 2:  Python for Natural Language Processing

Chapter Goal: A useful chapter for people focusing on how to setup your own python environment for NLP and also some basics on handling text data with python and coverage of popular open source frameworks for NLP

No of pages: 20 - 30

Sub - Topics

1. Setup Python for NLP

2. Handling strings with Python

3. Regular Expressions with Python

4. Quick glance into nltk, gensim, spacy, scikit-learn, keras 

Chapter 3:  Processing and Understanding Text

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:  Feature Engineering for Text Data

Chapter Goal: This chapter covers important strategies to extract meaningful features from unstructured text data. This includes traditional techniques as well as newer deep learning based methods. 

No of pages : 40 - 50

Sub - Topics:  

1. Feature engineering strategies for text data

2. Bag of words model

3. TF-IDF model

3. Bag of N-grams model

4.   Topic Models

5.   Word Embedding based models (word2vec, glove)

Chapter 5: 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: 30 - 40

Sub - Topics: 

1.  Classification basics

2.   Types of classifiers

4. Binary and multi-class classification models

5. Building a text classifier on real world data with machine learning

6. Some coverage of deep learning based classifiers

7. Evaluating Classifiers

Chapter 6: 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 (only if it performs well)

7. Basics on deep learning for summarization 

Chapter 7: 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 meas