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Sentiment Analysis Using Machine Learning Models
Yaya Liu
Yaya Liu
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Sentiment Analysis Using Machine Learning Models

Develop machine learning models to train computers to interpret text and predict whether a customer’s attitude is positive, negative or neutral towards a company or the service they received from the company based on text data from Twitter.

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Mondays
 at
5:00
P.M.
 ET /
2:00
P.M.
PT
8 weeks, 2-3 hours per week
Intermediate
No experience required
No experience required
Some experience required
Degree and experience required

Description

The airline industry is more competitive than it has ever been since more and more small and low-cost carriers entered the market. In order to increase business competitiveness, companies need to constantly assess and monitor how satisfied the customers are with the company and the service the company provides, as well as identify any potential negative trends on social media.

In contrast to traditional paper-based surveys, machine learning models provide a more timesaving, economical, and reliable way to identify and quantify customers’ opinions and attitudes, especially by utilizing massive data from social media. In this Build Project, you will build machine learning models to train computer software to interpret text and predict whether a customer’s attitude is positive, negative or neutral towards the service they received from six U.S. airline companies based on text data from Twitter. You will get exposed to Exploratory Data Analysis (EDA), text preprocessing, feature engineering, data balancing, model design, development and evaluation. You'll get exposed to the daily life of a Data Scientist and learn techniques that are must-to-know for any DS professional.

Session timeline

  • Applications open
    May 27, 2024
  • Application deadline
    June 23, 2024
  • Project start date
    Week of July 8, 2024
    Week of
    July 8, 2024
  • Project end date
    Week of

What you will learn

  • Write Python scripts to conduct Exploratory Data Analysis (EDA)
  • Utilize word embedding techniques to transform text data to numerical and machine-readable features
  • Build a machine learning pipeline from data preparation, model development to model evaluation
  • Evaluate model performance using different model evaluation metrics and identify which model provides better results
Build Projects are 8-week experiences that operate on a rolling basis. Selected participants engage in weekly live workshops with a Build Fellow and 2-15 other students.

Project workshops

1
Introductions & Get Familiar with GitHub/Git
2
Exploratory Data Analysis (EDA)
3
Text Data Preprocessing
4
Feature Engineering
5
Data Balancing
6
Model Exploration and Development
7
Model Evaluation and Comparison
8
Slides Creation and Project Presentation

Prerequisites

  • Experience with basic Python (loops, conditions, functions, libraries, simple algorithms)
  • Some first exposure to Python packages (pandas, matplotlib, sklearn)
  • A basic understanding of text preprocessing techniques, such as letter decapitalization, tokenization and stemming, etc.
  • A basic understanding of classification models
  • Knowledge of common model evaluation metrics, including accuracy, recall, precision and F1-score

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About the expert
Yaya Liu
Visit
Yaya
's Linkedin

I am a data scientist in the insurance industry. I am passionate about identifying patterns and trends in data, creating data visualizations, developing machine learning models and providing insights to business partners.

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