Project: Perform Sentiment Analysis with scikit-learn

In this project, we will learn the fundamentals of sentiment analysis and apply our knowledge to classify movie reviews as either positive or negative. We will use the popular IMDB dataset. Our goal is to use a simple logistic regression model from scikit-learn for document classification.

Join for Free
Project: Perform Sentiment Analysis with scikit-learn

Duration (mins)

Learners

NA / 5

Rating

Task List


We will cover the following tasks in 57 minutes:


Introduction and Importing the Data

In this task, we get an overview of this project and get ourselves familiar with the popular IMDB movie review dataset.


Transforming Documents into Feature Vectors

We will get a description of what logistic regression is and why we use it for sentiment analysis. Once we have clear idea of the features and model, we will encounter our first natural language processing concept. Namely, the bag-of-words model. From scikit-learn, we call the fit_transform method on CountVectorizer. This will construct the vocabulary of the bag-of-words model and transform the provided sample sentences into sparse feature vectors.


Term Frequency-Inverse Document Frequency (TF-IDF)

In information retrieval and text mining, we often observe words that crop up across our corpus of documents. These words can lead to bad performance during training and test time because they usually don’t contain useful information. In this task, we will understand and implement a useful statistical technique to mitigate this — Term frequency-inverse document frequency (tf-idf) can be used to downweight these class of words in our feature vector representation. The tf-idf is the product of the term frequency and the inverse document frequency.


Data Preparation

Cleaning and preprocessing text data is a vital process in data analysis and especially in natural language processing tasks. In this task, we will take a look at a few reviews from our dataset and learn how to strip them of irrelevant characters like HTML tags, punctuation, and emojis using regular expressions.


Tokenization of Documents

In this task, we learn how to represent our data as a collection of words or tokens. We will also perform word-level preprocessing tasks such as stemming. To accomplish this, we use The Natural Language Toolkit (nltk) in Python.


Transform Text Data into TF-IDF Vectors


Document Classification using Logistic Regression

First, we split our into training and test sets of equal size. We then create a pipeline to build a logistic regression model. To estimate the best parameters and model, we employ cross-validated grid-search over a parameter grid.


Model Evaluation

In this task, we load a pre-trained model that we will later use to find the best parameter settings, cross validation score, and the test accuracy. We also take a look at the best parameter settings, cross-validation score, and how well our model classifies the sentiments of reviews it has never seen before from the test set.

Watch Preview

Preview the instructions that you will follow along in a hands-on session in your browser.

Snehan Kekre

About the Host (Snehan Kekre)


Snehan Kekre is a Machine Learning and Data Science Instructor at Coursera. He studied Computer Science and Artificial Intelligence at Minerva Schools at KGI, based in San Francisco. His interests include AI safety, EdTech, and instructional design. He recognizes that building a deep, technical understanding of machine learning and AI among students and engineers is necessary in order to grow the AI safety community. This passion drives him to design hands-on, project-based machine learning courses on Rhyme.



Frequently Asked Questions


In Rhyme, all projects are completely hands-on. You don't just passively watch someone else. You use the software directly while following the host's (Snehan Kekre) instructions. Using the software is the only way to achieve mastery. With the "Live Guide" option, you can ask for help and get immediate response.
Nothing! Just join through your web browser. Your host (Snehan Kekre) has already installed all required software and configured all data.
Absolutely! Your host (Snehan Kekre) has provided this session completely free of cost!
You can go to https://rhyme.com, sign up for free, and follow this visual guide How to use Rhyme to create your own projects. If you have custom needs or company-specific environment, please email us at help@rhyme.com
Absolutely. We offer Rhyme for workgroups as well larger departments and companies. Universities, academies, and bootcamps can also buy Rhyme for their settings. You can select projects and trainings that are mission critical for you and, as well, author your own that reflect your own needs and tech environments. Please email us at help@rhyme.com
Rhyme strives to ensure that visual instructions are helpful for reading impairments. The Rhyme interface has features like resolution and zoom that will be helpful for visual impairments. And, we are currently developing a close-caption functionality to help with hearing impairments. Most of the accessibility options of the cloud desktop's operating system or the specific application can also be used in Rhyme. If you have questions related to accessibility, please email us at accessibility@rhyme.com
We started with windows and linux cloud desktops because they have the most flexibility in teaching any software (desktop or web). However, web applications like Salesforce can run directly through a virtual browser. And, others like Jupyter and RStudio can run on containers and be accessed by virtual browsers. We are currently working on such features where such web applications won't need to run through cloud desktops. But, the rest of the Rhyme learning, authoring, and monitoring interfaces will remain the same.
Please email us at help@rhyme.com and we'll respond to you within one business day.

Ready to join this 57 minutes session for free?

More Projects by Snehan Kekre