Four-Project Series

End-to-End Deep Learning for Opinion Mining you own this product

intermediate Python • basic pandas and NumPy • basics of data visualization • basics of Jupyter Notebook • basics of deep learning • basic PyTorch
skills learned
connect to an API to extract the data • clean and filter data from JSON • save data into MongoDB • Perform topic modeling using LDA and visualize the topics • Transfer learning using state-of-the-art NLP model • Deploy a Streamlit dashboard
Winnie Yeung and Eyan Yeung
4 weeks · 4-5 hours per week average · INTERMEDIATE

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In this series of liveProjects, you’ll use data science and natural language processing techniques to perform the kind of real-world work routinely conducted by data scientists in the marketing sector. You’ll build an effective solution that can scrape, analyze, and monitor chatter on a Reddit forum to determine the opinions of your company’s customers. Each project in this series can stand alone or be worked through together, as you go hands on with data collection, data exploration, utilizing transfer learning, and building effective data dashboards.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

liveProject mentor Lavanya Mysuru Krishnamurthy shares what she likes about the Manning liveProject platform.

here's what's included

Project 1 Web-scraping for Text Threads

In this liveProject, you’ll harvest customer opinions about your company’s products from the comments left on the subreddit for your company, and store them in a database for future analysis. You’ll connect to the Reddit API, identify and clean up the data fields you need, and store the data into MongoDB.

Project 2 Cleaning and Exploring Text Data

In this liveProject, you’ll clean and analyze data scraped from Reddit to determine customer opinions of your products within a set time period. You’ll utilize common natural language processing techniques such as stemming, tokenization, and latent dirichlet allocation (LDA) to discover patterns in people’s opinions, and then visualize your results and summarize your findings.

Project 3 Transfer Learning with Transformers

In this liveProject, you’ll use transformer-based deep learning models to predict the tag of Reddit subreddits to help your company understand what its customers are saying about them. Transformers are the state of the art, large-scale deep language models pretrained on a huge corpus of text, and are capable of understanding the complexity of grammar really well. You’ll train this model on your own data set, and tune its hyperparameters for the best results.

Project 4 Deploy a Streamlit Dashboard

In this liveProject, you’ll build an interactive dashboard that will allow the marketing team at your company to monitor any mention of your company’s products on Reddit. You’ll start by visualizing relevant subreddit data, then build a model to monitor your mentions on Reddit. Finally, you’ll deploy the Streamlit dashboard on Heroku. Streamlit offers a simple and easy way to build a highly interactive and beautiful dashboard with just a few lines of codes, whilst Heroku offers free web hosting for data apps.

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  • End-to-End Deep Learning for Opinion Mining project for free

project authors

Man Wai Winnie Yeung
Winnie Yeung is a full-stack senior data scientist at Visa in the San Francisco Bay Area, working on developing and deploying risk-related machine learning solutions. She earned her master’s in analytics at Georgia Institute of Technology and has 3 years of experience working on natural language processing projects in the investment industry. She actively contributes to the open-source community by creating a neural machine translation package on PyPI, as well as giving talks at PyCon Hong Kong.
Eyan Yeung
Eyan Yeung, PhD is a full-stack data scientist in New Jersey using various machine learning models and data science techniques to fight adversarial abuse. She earned her PhD in molecular biology at Princeton University, having used unsupervised machine learning techniques and built mathematical models to analyze large-scale biological datasets. She has experience completing multiple end-to-end projects in image classification and natural language processing.


This liveProject is for confident Python programmers interested in taking their first steps into data analysis for marketing. To begin this liveProject you will need to be familiar with the following:

  • Intermediate Python
  • Basics of Jupyter Notebook
  • Basic NumPy
  • Basic pandas
  • Basic seaborn
  • Basic API call
  • Basic GitHub/Git
  • Basics of scikit-learn
  • Basics of PyTorch
  • Basics of Matplotlib
  • Basic Git
  • Basics of databases
  • Intermediate exploratory data analysis
  • Basic knowledge of neural networks
  • Basic concepts in machine learning
  • Basic data visualization

Note: The final milestone of Project 4 Deploy a Streamlit Dashboard uses Heroku to demo the completed app. Heroko incurs a cost. There is intermittent use, and the Eco option ($5) will be sufficient to get the app working as Eco covers 1000 hours, and we will be using far less than that for this project.

you will learn

In this liveProject, you’ll learn to build data science dashboards, which are an essential final deliverable for data projects.

  • Connect to an API and only extract the data you need
  • Clean and filter for necessary data from JSON
  • Save data into MongoDB in an organized manner
  • Query the database for simple analytic tasks
  • Checking for and cleaning up null and duplicate data
  • Drilling into selective threads/replies
  • Engineer new columns using pandas to obtain summary statistics about the replies and threads
  • NLP preprocessing techniques such as stemming, n-gram, and removal of stop words using NLTK
  • Visualize word cloud and top n-grams
  • Perform topic modeling using LDA and visualize the topics
  • Summarize findings and use graphs to support your points
  • Data preprocessing for PyTorch models
  • Data augmentation
  • Model monitoring
  • Transfer learning using state-of-the-art NLP model
  • Model diagnostics
  • Version control of models
  • Generate inferences on unseen data using a fine-tuned model
  • Dashboard design
  • Data visualization
  • Integrating API call with dashboard
  • Version control using GitHub
  • Deploy data apps and dashboards using Heroku


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.