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Perform Sentiment Analysis with scikit-learn
Snehan Kekre
Snehan Kekre
In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification.
Read more
In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification.
Read more
In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/gyp7rv/perform
What's inside
Syllabus
Project: Perform Sentiment Analysis with scikit-learn
In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides an introduction to the fundamentals of sentiment analysis, which is a valuable tool for businesses and individuals seeking to understand the sentiment expressed in text data
Emphasizes practical application by guiding learners through building a logistic regression model using scikit-learn, a popular machine learning library
Utilizes the well-known IMDB dataset, providing learners with a real-world context for their project
Leverages Rhyme, a hands-on project platform that provides instant access to a cloud desktop with pre-configured software and data, ensuring a smooth learning experience
Well-suited for learners interested in gaining a foundational understanding of sentiment analysis and applying it to practical problems
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Save Perform Sentiment Analysis with scikit-learn to your list so you can find it easily later:
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Reviews summary
Scikit-learn sentiment analysis
Learners say that Perform Sentiment Analysis with scikit-learn is a good, very useful course that's very good for beginners with little NLP experience. Students say that the practical content is well received, but there are a few issues with the cloud IDE. Overall, learners say that the course is well-designed and engaging.
Suitable for beginners
"This project is very useful for people that don't know anything about sentiment analysis"
"As a beginner in Data Science, who only knows ML concepts and Exploratory Data Analysis techniques, I really liked this project."
"Nicely explained and very good for those who don't have any basics in NLP"
Course is practice-based
"I love the part that you had to write your codes as the teacher was teaching."
"It's really a very good course for a beginner"
"Very clear, very practical... well guided..."
Some issues with cloud IDE
"Had some trouble with the cloud IDE at the beginning, but overall a nice course"
"It'll be better if access time for cloud desktop is not limited."
"The practical session wasn't available."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Perform Sentiment Analysis with scikit-learn with these
activities:
Read 'Machine Learning for Dummies'
Show steps
Gain a comprehensive understanding of machine learning concepts to supplement your knowledge in this course.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
-
Read chapters relevant to sentiment analysis
-
Complete exercises and practice problems
Refresh Python Basics
Show steps
Refresh your memory on the core Python concepts and syntax before starting the course to ensure a stronger foundation.
Browse courses on
Python
Show steps
-
Review official Python tutorials
-
Solve basic programming challenges
Compile a List of Sentiment Analysis Resources
Show steps
Gather and organize a collection of useful resources on sentiment analysis, such as tutorials, datasets, and tools, for future reference.
Show steps
-
Search for online resources
-
Organize resources into categories
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow Natural Language Processing Tutorials
Show steps
Enhance your understanding of natural language processing, which is essential for sentiment analysis, by working through guided tutorials.
Show steps
-
Identify relevant tutorials
-
Complete tutorials and practice exercises
Complete Sentiment Analysis Drills
Show steps
Practice sentiment analysis on sample datasets to solidify your understanding of the techniques and algorithms used in this course.
Show steps
-
Use online sentiment analysis tools
-
Analyze sample movie reviews
Sentiment Analysis Practice Exercises
Show steps
Gain proficiency in sentiment analysis techniques by solving various practice problems.
Browse courses on
Sentiment Analysis
Show steps
-
Identify platforms or resources that offer sentiment analysis practice problems.
-
Solve practice problems of varying difficulty levels.
-
Analyze your results and identify areas for improvement.
Tutorial: Fine-Tune Logistic Regression Model
Show steps
Understand the inner workings and refine the logistic regression model used for sentiment analysis.
Browse courses on
Logistic Regression
Show steps
-
Find informative tutorials on model fine-tuning.
-
Follow the steps to fine-tune the model.
-
Evaluate the performance of the fine-tuned model.
Develop a Sentiment Analysis Model
Show steps
Build a logistic regression model for sentiment analysis on a dataset of your choice to apply your knowledge and deepen your understanding of the concepts.
Show steps
-
Gather and prepare a dataset
-
Write code to implement the model
-
Evaluate the model's performance
Contribute to Scikit-Learn
Show steps
Deepen your understanding of sentiment analysis and machine learning algorithms by contributing to the development of the Scikit-Learn library.
Show steps
-
Review Scikit-Learn documentation
-
Identify an area to contribute
-
Submit a pull request
Read 'Machine Learning for Dummies'
Show steps
Gain a comprehensive understanding of machine learning concepts to supplement your knowledge in this course.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to sentiment analysis
- Complete exercises and practice problems
Refresh Python Basics
Show steps
Refresh your memory on the core Python concepts and syntax before starting the course to ensure a stronger foundation.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review official Python tutorials
- Solve basic programming challenges
Compile a List of Sentiment Analysis Resources
Show steps
Gather and organize a collection of useful resources on sentiment analysis, such as tutorials, datasets, and tools, for future reference.
Show steps
Show steps
Show steps
- Search for online resources
- Organize resources into categories
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow Natural Language Processing Tutorials
Show steps
Enhance your understanding of natural language processing, which is essential for sentiment analysis, by working through guided tutorials.
Show steps
Show steps
Show steps
- Identify relevant tutorials
- Complete tutorials and practice exercises
Complete Sentiment Analysis Drills
Show steps
Practice sentiment analysis on sample datasets to solidify your understanding of the techniques and algorithms used in this course.
Show steps
Show steps
Show steps
- Use online sentiment analysis tools
- Analyze sample movie reviews
Sentiment Analysis Practice Exercises
Show steps
Gain proficiency in sentiment analysis techniques by solving various practice problems.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Identify platforms or resources that offer sentiment analysis practice problems.
- Solve practice problems of varying difficulty levels.
- Analyze your results and identify areas for improvement.
Tutorial: Fine-Tune Logistic Regression Model
Show steps
Understand the inner workings and refine the logistic regression model used for sentiment analysis.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find informative tutorials on model fine-tuning.
- Follow the steps to fine-tune the model.
- Evaluate the performance of the fine-tuned model.
Develop a Sentiment Analysis Model
Show steps
Build a logistic regression model for sentiment analysis on a dataset of your choice to apply your knowledge and deepen your understanding of the concepts.
Show steps
Show steps
Show steps
- Gather and prepare a dataset
- Write code to implement the model
- Evaluate the model's performance
Contribute to Scikit-Learn
Show steps
Deepen your understanding of sentiment analysis and machine learning algorithms by contributing to the development of the Scikit-Learn library.
Show steps
Show steps
Show steps
- Review Scikit-Learn documentation
- Identify an area to contribute
- Submit a pull request
Career center
Learners who complete Perform Sentiment Analysis with scikit-learn will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design, build, and maintain systems that can understand and respond to human language. They use sentiment analysis to train models that can identify the sentiment of text data. This course provides an overview of sentiment analysis for NLP, a field in which sentiment analysis is essential.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They use sentiment analysis to train models that can understand and respond to human language. This course provides Machine Learning Engineers with an overview of sentiment analysis and machine learning, a powerful combination in this field.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They use sentiment analysis to identify trends, predict customer behavior, and make recommendations. This course provides Data Scientists with an overview of sentiment analysis, a skill that has become increasingly important to the role.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, build, and maintain artificial intelligence systems. They use sentiment analysis to train models that can understand and respond to human language. This course could be useful for building a foundation in sentiment analysis and machine learning, both of which are essential to the work of an AI Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They use sentiment analysis to identify trends in the market, and to make predictions about future stock prices. Specifically, this course provides the Quant Analyst with an opportunity to learn about sentiment analysis and its application in machine learning, a powerful tool for investment analysis.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They use sentiment analysis to understand customer feedback, identify product pain points, and make improvements. Specifically, this course helps Product Managers build a foundation in sentiment analysis as it applies to machine learning. This could be particularly useful for product development and improvement.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They use sentiment analysis to identify trends in the market, and to make predictions about future stock prices. This course provides an overview of sentiment analysis, a skill that can be useful to Financial Analysts who wish to improve their decision-making and enhance their analysis of market trends.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. They use sentiment analysis to improve the user experience, identify bugs, and make software more responsive to user needs. This course may be useful in helping to build a foundation in sentiment analysis, a skill that has become increasingly necessary in software engineering.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. They use sentiment analysis to understand customer feedback, identify trends, and make recommendations. This course could be useful for building a foundation in sentiment analysis, a skill that is increasingly important in the field of business analysis.
Market Research Analyst
Market Research Analyst
Market Research Analysts study market conditions and consumer trends to help companies understand their customers. They conduct surveys, interviews, and focus groups, and apply sentiment analysis to gauge the public's interest in a company or brand. This course could be useful in helping learn the fundamentals of sentiment analysis, a skill that could enhance one's approach to market research.
Data Analyst
Data Analyst
Data Analysts are responsible for culling insights from data. Familiarity with natural language processing (NLP) tools like sentiment analysis is becoming a requirement in the field. Platforms like Coursera can help Data Analysts who wish to learn more about NLP to advance their careers and optimize their analytical skill sets.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics. They use sentiment analysis to understand customer feedback, identify trends, and make recommendations. This course could be useful for Consultants who wish to build a foundation in sentiment analysis, a skill that can complement their advisory services.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. They use sentiment analysis to understand customer feedback, identify trends, and make decisions about their products or services. This course could be useful for budding Entrepreneurs who wish to add sentiment analysis to their skill-set, which may be useful in business endeavors including marketing and product development.
Marketing Manager
Marketing Manager
Marketing Managers develop advertising campaigns that appeal to target audiences. Sentiment analysis can help them understand how customers feel about their products or services, and how to better tailor their marketing messages. This course may be useful in helping to build a foundation in sentiment analysis for this purpose.
Research Analyst
Research Analyst
Research Analysts contribute to the success of investment funds and banking operations by using data analysis and forecasting to evaluate opportunities. Sentiment Analysis is integral to their work. Research Analysts use it to sort through large data sets from a variety of channels to extract information that can help plan future strategies. This course may be useful in helping build a foundation in using this valuable technique.
For more career information including salaries, visit:
OpenCourser.com/course/gyp7rv/perform
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Perform Sentiment Analysis with scikit-learn.
Provides a comprehensive overview of sentiment analysis and opinion mining, focusing on both the theoretical and practical aspects of the field. It covers topics such as sentiment analysis techniques, opinion mining algorithms, and applications of sentiment analysis in various domains.
Provides a comprehensive introduction to machine learning algorithms and how to use them in practice with scikit-learn, Keras, and TensorFlow. It covers topics such as data preprocessing, linear regression, classification, clustering, and dimensionality reduction. The book is hands-on, with plenty of code examples and exercises.
Provides a comprehensive introduction to machine learning algorithms and how to use them in practice with scikit-learn. It covers topics such as data preprocessing, linear regression, classification, clustering, and dimensionality reduction. The book is hands-on, with code examples and exercises throughout.
Provides a comprehensive introduction to machine learning and deep learning using Python, scikit-learn, and TensorFlow 2. It covers topics such as data preprocessing, linear regression, classification, clustering, and dimensionality reduction. The book is hands-on, with plenty of code examples and exercises.
Provides a practical guide to natural language processing for developers. It covers topics such as text preprocessing, feature engineering, text classification, and sentiment analysis. The book is hands-on, with plenty of code examples and exercises.
Provides a hands-on introduction to natural language processing using Python. It covers topics such as text preprocessing, feature engineering, text classification, and sentiment analysis. The book is hands-on, with plenty of code examples and exercises.
Provides a practical introduction to text analytics using Python. It covers topics such as text preprocessing, feature engineering, text classification, and sentiment analysis. The book is hands-on, with plenty of code examples and exercises.
Introduces fundamental concepts, algorithms, and techniques of natural language processing, providing a solid understanding of the field and how to apply it to real-world natural language tasks. The book includes coverage of topics such as text classification, sentiment analysis, named entity recognition, and machine translation. It valuable resource for anyone interested in natural language processing and machine learning.
Save
Provides a practical introduction to text mining using the R programming language. It covers topics such as text cleaning, feature engineering, text classification, and topic modeling. The book is hands-on, with plenty of code examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gyp7rv/perform
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Provides an introduction to the fundamentals of sentiment analysis, which is a valuable tool for businesses and individuals seeking to understand the sentiment expressed in text data
Emphasizes practical application by guiding learners through building a logistic regression model using scikit-learn, a popular machine learning library
Utilizes the well-known IMDB dataset, providing learners with a real-world context for their project
Leverages Rhyme, a hands-on project platform that provides instant access to a cloud desktop with pre-configured software and data, ensuring a smooth learning experience
Well-suited for learners interested in gaining a foundational understanding of sentiment analysis and applying it to practical problems
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