Text Analytics with Python: A Practitioner's Guide to Natural Language Processing (2 Ed.)

Chapter 1: Natural Language BasicsChapter Goal: Introduces the readers to the basics of NLP and Text processingNo of pages: 40 - 50 Sub -Topics1. Language Syntax and Structure2. Text formats and grammars3. Lexical and Text Corpora resources4. Deep dive into the Wordnet corpus5. Parts of speech, Stemming and lemmatizationChapter 2: Python for Natural Language ProcessingChapter 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 NLPNo of pages: 20 - 30Sub - Topics 1. Setup Python for NLP2. Handling strings with Python3. Regular Expressions with Python4. Quick glance into nltk, gensim, spacy, scikit-learn, keras Chapter 3: Processing and Understanding TextChapter 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 - 40Sub - Topics: 1. Sentence and word tokenization2. Text tagging and chunking3. Text Parse Trees3. Text normalization4. Text spell checks and removal of redundant characters5. Synonyms and SynsetsChapter 4: Feature Engineering for Text DataChapter 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 - 50Sub - Topics: 1. Feature engineering strategies for text data2. Bag of words model3. TF-IDF model3. Bag of N-grams model4. Topic Models5. Word Embedding based models (word2vec, glove)Chapter 5: Text ClassificationChapter Goal: Introduces readers to the concept of classification as a supervised machine learning problem and looks at a real world example for classifying text documentsNo of pages: 30 - 40Sub - Topics: 1. Classification basics2. Types of classifiers3. Feature generation of text documents4. Binary and multi-class classification models5. Building a text classifier on real world data with machine learning6. Some coverage of deep learning based classifiers7. Evaluating ClassifiersChapter 6: Text summarization and topic modelingChapter 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 sameNo of pages: 40 - 45Sub - Topics: 1. Text summarization concepts2. Dimensionality reduction3. N-gram tagging models4. Topic modeling using LDA and LSA5. Generate topics from real world data6. 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 analysisChapter Goal: We look at unsupervised machine learning concepts here like text clustering and similarity measuresNo of pages: 35 - 40Sub - Topics: 1. Clustering concepts2. Analyzing text similarity3. Implementing text similarity with cosine, jaccard meas