Growth Hacking with NLP and Sentiment Analysis

Sentiment analysis, Natural language processing, Deep learning, Classification, Transformers
Orsolya Putz and Zoltan Varju
5 weeks · 8-10 hours per week
In this liveProject, 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.

project authors

Orsolya Putz
Orsolya Putz holds a PhD in Cognitive Linguistics and devotes herself to cognitive sciences. Currently she is an assistant lecturer at Eötvös Loránd University, Budapest and the co-founder of Crow Intelligence, a boutique consultancy specialized in NLP and AI. Her main research areas are cognitive metaphor theory, text analytics, and cognitive background of biases in human and machine models. She also worked as a linguistic expert on various text analytics projects.
Zoltan Varju
Zoltán Varjú has been working as an expert in Natural Language Processing for 15 years. He was the head of several text analytics and enterprise search projects in the financial and health sectors. Having led Data Science and NLP teams at small companies, large corporations, and NGOs, now he is building his own enterprise, called Crow Intelligence, a boutique consultancy specialized in NLP and AI. He is the founder of the Hungarian Natural Language Processing Meetup.


This liveProject 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:

  • Python standard library
  • Basics of pandas
  • Basics of Jupyter Notebook
  • Basics of Colab
  • Basics of scikit-learn
  • Basics of machine learning
  • Basics of neural networks

you will learn

In this liveProject, you’ll learn the foundational techniques of an NLP Specialist using the Python data ecosystem. The sentiment analysis skills you’ll learn are all easily transferable to other common NLP projects.

  • 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


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Peer support
Chat with other participants within the liveProject platform.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
Book and video resources
Excerpts from Manning books and videos are included, as well as references to other resources.

project outline


Prerequisites Test

about this liveProject

1. Creating your dataset

1.1. Creating your dataset

1.2. Analyzing Tables Using Pandas

1.3. Running Random Simulations in NumPy

1.4. Introducing Annotation

1.5. Submit Your Work


2. Creating a dictionary-based sentiment analyzer

2.1. Creating a dictionary-based sentiment analyzer

2.2. Build Your Vocabulary (Word Tokenization)

2.3. Submit Your Work


3. Evaluating your dictionary-based sentiment analyzer

3.1. Evaluating your dictionary-based sentiment analyzer

3.2. Model evaluation and optimization

3.3. Submit Your Work


4. Creating neural network based sentiment analyzers

4.1. Creating neural network based sentiment analyzers

4.2. Introducing Deep Learning and the PyTorch Library

4.3. Model optimization through parameter tuning

4.4. Introducing NLP in practice: spam filtering

4.5. What is transfer learning?

4.6. Submit Your Work


5. Finding key phrases and writing a report

5.1. Finding key phrases and writing a report

5.2. Submit Your Work


6. Summary

6.1. Project Conclusions

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