Duration
2 days
What you'll learn
Understand the various concepts of natural language processing along with their implementation
Build natural language processing based applications
Learn about the different modules available in Python for NLP
Create personal text summarizer
Essential concepts of Social Network Analysis
Network centrality measures
Community structure
Community detection algorithms
Network diffusion models
Visualize and analyze the Tweeter networks
To work with SNA tools such as Gephi, Pajeck, UCINET
Who this course is for
Anyone willing to start a career in data science and natural language processing
Anyone willing to learn the concepts of natural language processing by implementing them
Anyone willing to learn Sentiment Analysis and Social Media Analytics
Course Outline
Introduction to the Course
What is NLP?
Installing Anaconda Python
Installing Anaconda Python - Text
A tour of Spyder IDE Regular Expressions
Introduction to Regular Expressions
Finding Patterns in Text
Substituting Patterns in Text
Shorthand Character Classes
Character Ranges - Text
Preprocessing using Regex
NLP Core
Installing NLTK in Python
Tokenizing Words and Sentences
How tokenization works? - Text
Introduction to Stemming and Lemmatization
Stemming using NLTK
Lemmatization using NLTK
Stop word removal using NLTK
Parts Of Speech Tagging
POS Tag Meanings
Named Entity Recognition
Text Modelling using Bag of Words Model
Building the BOW Model
Text Modelling using TF-IDF Model
Understanding the N-Gram Model
Understanding Latent Semantic Analysis
Word Synonyms and Antonyms using NLTK
Word Negation Tracking in Python
Project 1 - Text Classification
Getting the data for Text Classification
Getting the data for Text Classification - Text
Importing the dataset
Persisting the dataset
Preprocessing the data
Transforming data into BOW Model
Transform BOW model into TF-IDF Model
Creating training and test set
Understanding Logistic Regression
Training our classifier
Testing Model performance
Saving our Model
Importing and using our Model
Project 2 - Twitter Sentiment Analysis
Setting up Twitter Application
Initializing Tokens
Client Authentication
Fetching real time tweets
Loading TF-IDF Model and Classifier
Preprocessing the tweets
Predicting sentiments of tweets
Plotting the results
Project 3 - Text Summarization
Understanding Text Summarization
Fetching article data from the web
Parsing the data using Beautiful Soup
Preprocessing the data
Tokenizing Article into sentences
Building the histogram
Calculating the sentence scores
Getting the summary
Word2Vec Analysis
Understanding Word Vectors
Importing the data
Preparing the data
Training the Word2Vec Model
Testing Model Performance
Improving the Model
Exploring Pre-trained Models
Social Media Analytics
Introduction to Networks
Introduction to Social Network Analysis
What are Social Network Analysis tools?
SNA tools, pajek, , UCINET, Gephi, NodeXL, Sci2
Measuring Network Centrality by Different SNA Tools
Concepts: SNA tools, betweenness, closeness, eigenvector centrality , PageRank, network centralization
Small world network models
Complex contagion, adoption, mobilization
Diffusion and topology
Programme scope, delivery arrangements and suitability are confirmed during enquiry.