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Growth Hacking with NLP and Sentiment Analysis

  • Course Code: Data Science - Growth Hacking with NLP and Sentiment Analysis
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for those who wants to step into the role of a Natural Language Processing Specialist working in the Growth Hacking Team of a new video game startup.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level 3D Growth Hacking with NLP and Sentiment Analysis skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to step into the role of a Natural Language Processing Specialist working in the Growth Hacking Team of a new video game startup. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In this course, you’ll step into the role of a Natural Language Processing Specialist working in the Growth Hacking Team of a new video game startup. Your team wants to massively accelerate your company’s early growth by acquiring huge numbers of customers at the lowest possible cost. To help tailor marketing messages, your boss has asked you to map the market and find out how customers evaluate your competitors’ products. Your challenge is to create a sentiment analyzer that will give a deeper understanding of customer feedback and opinions. To do this, you’ll need to download and create a dataset from Amazon reviews, build an algorithm that will determine whether a review is positive or negative, evaluate your analyzer’s performance against star ratings, and lay out your findings in a report for your manager. 

Working in a hands-on learning environment, led by our Growth Hacking with NLP and Sentiment Analysis expert instructor, students will learn about and explore: 

  • Creating a data corpus from text reviews 
  • Sampling from imbalanced data 
  • Finding sentiment value using NLTK and dictionary-based sentiment analysis tools 
  • Data evaluation with scikit-learn 
  • Analyzing reviews using PyTorch and deep learning 
  • Comparing classifier performance 
  • Transformers-based language models 
  • Visualizing findings and presenting a formal report 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • CREATING YOUR DATASET 
  • CREATING A DICTIONARY-BASED SENTIMENT ANALYZER 
  • EVALUATING YOUR DICTIONARY-BASED SENTIMENT ANALYZER 
  • CREATING NEURAL NETWORK BASED SENTIMENT ANALYZERS 
  • FINDING KEY PHRASES AND WRITING A REPORT 

Audience & Pre-Requisites 

This course is for intermediate Python programmers who are familiar with data science. You will need to know the basics of statistics and machine learning. Previous encounters with NLP, neural networks, and PyTorch will be useful, but not essential. You’ll use the Google Collaboratory (Colab) environment for this project to access a free cloud-based GPU. To get the most out of the project, you should be familiar with. 

Pre-Requisites:  Students should have familiar with: 

TOOLS 

  • Python standard library 
  • Basics of pandas 
  • Basics of Jupyter Notebook 
  • Basics of Colab 
  • Basics of scikit-learn 

TECHNIQUES 

  • Basics of machine learning 
  • Basics of neural networks 

Course Agenda / Topics 

  1. CREATING YOUR DATASET 
  • Creating your dataset 
  • Analyzing Tables Using Pandas 
  • Running Random Simulations in NumPy 
  • Introducing Annotation 
  1. CREATING A DICTIONARY-BASED SENTIMENT ANALYZER 
  • Creating a dictionary-based sentiment analyzer 
  • Build Your Vocabulary (Word Tokenization) 
  1. EVALUATING YOUR DICTIONARY-BASED SENTIMENT ANALYZER 
  • Evaluating your dictionary-based sentiment analyzer 
  • Model evaluation and optimization 
  1. CREATING NEURAL NETWORK BASED SENTIMENT ANALYZERS 
  • Creating neural network-based sentiment analyzers 
  • Introducing Deep Learning and the PyTorch Library 
  • Model optimization through parameter tuning 
  • Introducing NLP in practice: spam filtering 
  • What is transfer learning? 
  1. FINDING KEY PHRASES AND WRITING A REPORT 
  • Finding key phrases and writing a report 
  1. SUMMARY 
  • Project Conclusions 
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