Course
Sentiment Analysis in Python
IntermediateSkill Level
Updated 02/2024PythonMachine Learning4 hr16 videos60 Exercises5,050 XP23,167Statement of Accomplishment
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Prerequisites
Python Toolbox1
Sentiment Analysis Nuts and Bolts
Have you ever checked the reviews or ratings of a product or a service before you purchased it? Then you have very likely came face-to-face with sentiment analysis. In this chapter, you will learn the basic structure of a sentiment analysis problem and start exploring the sentiment of movie reviews.
2
Numeric Features from Reviews
Imagine you are in the shoes of a company offering a variety of products. You want to know which of your products are bestsellers and most of all - why. We embark on step 1 of understanding the reviews of products, using a dataset with Amazon product reviews. To that end, we transform the text into a numeric form and consider a few complexities in the process.
3
More on Numeric Vectors: Transforming Tweets
This chapter continues the process of understanding product reviews. We will cover additional complexities, especially when working with sentiment analysis data from social media platforms such as Twitter. We will also learn other ways to obtain numeric features from the text.
4
Let's Predict the Sentiment
We employ machine learning to predict the sentiment of a review based on the words used in the review. We use logistic regression and evaluate its performance in a few different ways. These are some solid first models!
Sentiment Analysis in Python
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FAQs
What Python packages are used for sentiment analysis in this course?
You use nltk for natural language processing tasks and scikit-learn for building machine learning models. These tools handle text preprocessing, feature extraction, and classification.
What real-world datasets will I analyze?
You work with movie reviews, Amazon product reviews, and tweets from US airline passengers on Twitter. The final project involves end-to-end sentiment analysis of the airline tweets.
Does the course cover machine learning for predicting sentiment?
Yes. Chapter 4 teaches you to use logistic regression to predict review sentiment based on text features, and to evaluate model performance using multiple methods.
How does the course convert text into numeric features for models?
Chapters 2 and 3 teach techniques for transforming text into numeric vectors, covering both standard methods and approaches specific to social media data like tweets.
Is this course suitable for someone new to NLP?
It is an intermediate course. You need Intermediate Python and Introduction to Functions in Python, but no prior NLP experience is required to get started.
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