Sentiment Analysis and Natural Language Processing for Marketing

prerequisites: Intermediate Python, Beginner scikit-learn, Beginner PyTorch, Basics of NLP, Basics of Deep Learning
skills learned: Perform sampling from imbalanced data, Dictionary-based sentiment analysis, Analyze reviews with Deep Learning, Compare classifier performance

Orsolya Putz and Zoltan Varju

5 weeks · 8-10 hours per week · ADVANCED

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.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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.

Prerequisites

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:

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

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

features

Self-paced: 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.
Get Help: While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others: For each step, compare your deliverable to the solutions by the author and other participants.
book resources: Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

project outline

Introduction

Prerequisites Test

New module

Start Project

1. Creating your dataset

1.1. Creating your dataset

Analyzing Tables Using Pandas

Running Random Simulations in NumPy

Introducing Annotation

1.2. Submit Your Work

2. Creating a dictionary-based sentiment analyzer

2.1. Creating a dictionary-based sentiment analyzer

Build Your Vocabulary (Word Tokenization)

Sentiment

2.2. Submit Your Work

3. Evaluating your dictionary-based sentiment analyzer

3.1. Evaluating your dictionary-based sentiment analyzer

Model evaluation and optimization

Evaluation of Classification Models

3.2. Submit Your Work

4. Creating neural network based sentiment analyzers

4.1. 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?

4.2. Submit Your Work

5. Finding key phrases and writing a report

5.1. Finding key phrases and writing a report

Extending Your Vocabulary with n-grams

5.2. Submit Your Work

6. Summary

6.1. Project Conclusions

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