Sentiment Analysis Using Machine Learning Models

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.