Why you should do this Guided Project

Sentiment analysis is an important part of NLP that identifies the emotional tone behind a body of text and is used in customer reviews and survey responses, online and social media. In this project, you will determine the sentiment of movie reviews as positive, negative, and neutral. We start off with a rule-based method, then use Machine Learning, explaining the connection between the two. You will use Pandas to load, analyze and process your data. Then use sklearn to transform your data with Bag-Of-Words, or Term Frequency–Inverse Document Frequency transforms, then find the Sentiment using Machine Learning. Streamline the process apply Machine learning pipelines, and perform Hyperparameter selections in one shot. Finally, use libraries like the Natural language tool kit to improve performance. Each section will have toy examples so you can better wrap your head around it.

A Look at the Project Ahead

Understand Sentiment analysis
Apply pandas to load,analyze and process your data
Understand text preprocessing
Understand the connection between rule-based methods and Machine Learning based methods
Understand and Apply Bag-Of-Words and Term Frequency–Inverse Document Frequency to Sentiment analysis using
Apply Hyperparameter using scikit-learn to NLP
Apply Machine Learning pipeline using scikit-learn to NLP

What You'll Need

You will need to know how to program in Python and be somewhat familiar with Pandas and sklearn and logistic regression.

Building a Machine Learning Pipeline For NLP

Language

Topic

Industries

Enrollment Count

Skills You Will Learn

Offered By

Estimated Effort

Platform

Last Update