Opinion Mining

Cleaning and Exploring Text Data you own this product

This project is part of the liveProject series End-to-End Deep Learning for Opinion Mining
prerequisites
basic pandas and seaborn • intermediate exploratory data analysis
skills learned
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
Winnie and Eyan Yeung
1 week · 2-4 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series End-to-End Deep Learning for Opinion Mining liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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Look inside

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.

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

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.

prerequisites

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:


TOOLS
  • Basic pandas
  • Basic seaborn
TECHNIQUES
  • Intermediate exploratory data analysis

you will learn

In this liveProject, you’ll explore text preprocessing and NLP analysis techniques.


  • 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

features

Self-paced
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Project roadmap
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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.
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