We may earn an affiliate commission when you visit our partners.
Allen O'Neill
This course is packed with
everything you need to know to understand the field and be confident
about getting started in production settings.
Read more
This course is packed with everything you need to know to understand the field and be confident about getting started in production settings.
Read more
This course is packed with everything you need to know to understand the field and be confident about getting started in production settings.
Sentiment analysis is something humans take for granted, but can be a significant challenge for a machine. In this course, Understanding Sentiment Analysis and Its Applications, you’ll gain the ability to plan and understand how to apply the main techniques required for successful data analysis in this area of NLP. First, you’ll explore what we mean by "sentiment analysis". Next, you’ll discover the general theory and core concepts of the field. Finally, you’ll learn how to approach practical problems and challenges as you move from theory into the real world of production. When you’re finished with this course, you’ll have the skills and knowledge of sentiment analysis needed to discuss the topic with confidence and have a solid grounding to enable you to start planning out a production implementation to analyze sentiment.
This course is no longer available. Find something similar by browsing:
Sentiment Analysis
Natural Language Processing
Machine Learning
Data Analysis
Programming
Register for this course and see more details by visiting:
OpenCourser.com/course/gj5vw8/understanding
What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops skills and knowledge in sentiment analysis, which is core for NLP
Provides a solid foundation for learners to start planning out a production implementation to analyze sentiment
Builds a strong foundation in general theory and core concepts of sentiment analysis
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Practical introduction to sentiment analysis
According to students, this course provides a clear and concise overview of sentiment analysis, making it an excellent introduction for those new to the field or looking to grasp its fundamentals. Learners frequently commend the instructor's ability to simplify complex concepts and appreciate the focus on practical examples and real-world applications, particularly the valuable module on production settings. While it offers a solid foundational understanding, some advanced learners or experienced practitioners noted that the course could benefit from more in-depth coverage of advanced techniques and extensive coding exercises, suggesting it is best suited for beginners seeking a strong starting point rather than a deep dive into complex implementation.
Material appears to be updated and refined over time.
"Some of the older material felt a little less polished than the newer sections, suggesting it might have been updated over time."
"The content felt fresh and relevant, indicating continuous improvement in the course material."
"I appreciated that the course materials seemed to reflect current best practices in the field."
Focuses on real-world use and production readiness.
"The practical examples were incredibly helpful for understanding real-world applications."
"The module on production settings was particularly valuable."
"I now feel equipped to discuss and plan my first sentiment analysis project at work."
Offers a strong, understandable foundation.
"This course is an excellent introduction to sentiment analysis. The instructor explains complex concepts clearly..."
"Absolutely fantastic! The course demystifies sentiment analysis and provides a solid foundation..."
"A perfect starting point! I had very little background in NLP and this course broke down sentiment analysis into digestible parts."
Ideal for new learners, less for advanced practitioners.
"I wished for more advanced techniques and deeper dive into model optimization. It's great for beginners, but experienced practitioners might find it a bit basic."
"The course provides a decent introduction, but lacks the depth needed for a professional setting without further study."
"It felt more like a high-level overview than a deep dive. Needed more code examples and advanced algorithms. Disappointed with the lack of depth."
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 Understanding Sentiment Analysis and Its Applications with these
activities:
Review Fundamentals of NLP
Show steps
Prepare by reviewing key concepts of NLP to be ready to build on them as you progress through the course modules.
Browse courses on
Natural Language Processing
Show steps
-
Review text normalization methods
-
Brush up on vectorization and text embedding
Organize Course Resources
Show steps
Improve retention by organizing course materials, including notes, assignments, and resources, in a structured and accessible manner.
Show steps
-
Create a dedicated folder or workspace for course materials
-
Organize materials by topic or module
Read 'Natural Language Processing with Python'
Show steps
Enhance your knowledge by reading a comprehensive book on NLP, covering key concepts and practical applications, including sentiment analysis.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read chapters on sentiment analysis and text classification
-
Complete exercises and examples on sentiment analysis
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete Sentiment Analysis Tutorials
Show steps
Expand your understanding by following guided tutorials on sentiment analysis, covering core concepts and practical implementation.
Browse courses on
Sentiment Analysis
Show steps
-
Follow tutorial on basic sentiment analysis techniques
-
Complete tutorial on advanced sentiment analysis methods
Join a Study Group
Show steps
Connect with fellow learners by joining a study group, facilitating discussions, knowledge sharing, and mutual support.
Show steps
-
Find or create a study group for this course
-
Actively participate in group discussions
-
Collaborate on assignments and projects
Solve NLP Practice Problems
Show steps
Sharpen your problem-solving skills by working through NLP practice problems, focusing on tasks related to sentiment analysis.
Browse courses on
Sentiment Analysis
Show steps
-
Identify sentiment in sample texts
-
Apply NLP algorithms to analyze sentiment
Assist Other Learners
Show steps
Reinforce your understanding by helping others, participating in discussion forums, answering questions, and providing constructive feedback.
Show steps
-
Identify opportunities to assist other learners
-
Provide support and guidance on course material
Build a Sentiment Analysis Model
Show steps
Gain hands-on experience by building a sentiment analysis model from scratch, applying the techniques and algorithms learned throughout the course.
Browse courses on
Sentiment Analysis
Show steps
-
Collect and pre-process text data
-
Choose and implement a sentiment analysis algorithm
-
Evaluate and refine the model
Review Fundamentals of NLP
Show steps
Prepare by reviewing key concepts of NLP to be ready to build on them as you progress through the course modules.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Review text normalization methods
- Brush up on vectorization and text embedding
Organize Course Resources
Show steps
Improve retention by organizing course materials, including notes, assignments, and resources, in a structured and accessible manner.
Show steps
Show steps
Show steps
- Create a dedicated folder or workspace for course materials
- Organize materials by topic or module
Read 'Natural Language Processing with Python'
Show steps
Enhance your knowledge by reading a comprehensive book on NLP, covering key concepts and practical applications, including sentiment analysis.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read chapters on sentiment analysis and text classification
- Complete exercises and examples on sentiment analysis
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete Sentiment Analysis Tutorials
Show steps
Expand your understanding by following guided tutorials on sentiment analysis, covering core concepts and practical implementation.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Follow tutorial on basic sentiment analysis techniques
- Complete tutorial on advanced sentiment analysis methods
Join a Study Group
Show steps
Connect with fellow learners by joining a study group, facilitating discussions, knowledge sharing, and mutual support.
Show steps
Show steps
Show steps
- Find or create a study group for this course
- Actively participate in group discussions
- Collaborate on assignments and projects
Solve NLP Practice Problems
Show steps
Sharpen your problem-solving skills by working through NLP practice problems, focusing on tasks related to sentiment analysis.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Identify sentiment in sample texts
- Apply NLP algorithms to analyze sentiment
Assist Other Learners
Show steps
Reinforce your understanding by helping others, participating in discussion forums, answering questions, and providing constructive feedback.
Show steps
Show steps
Show steps
- Identify opportunities to assist other learners
- Provide support and guidance on course material
Build a Sentiment Analysis Model
Show steps
Gain hands-on experience by building a sentiment analysis model from scratch, applying the techniques and algorithms learned throughout the course.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Collect and pre-process text data
- Choose and implement a sentiment analysis algorithm
- Evaluate and refine the model
Career center
Learners who complete Understanding Sentiment Analysis and Its Applications will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists apply their programming knowledge and statistical skills to solve real-world problems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve business problems.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure to support data analysis and machine learning.
Data Analyst
Data Analyst
Data Analysts analyze and interpret data to help businesses make better decisions.
Research Analyst
Research Analyst
Research Analysts study market and industry trends to provide insights and recommendations to businesses.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to help organizations make informed decisions.
Business Analyst
Business Analyst
Business Analysts help organizations improve their business processes and make better decisions.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations.
Sales Manager
Sales Manager
Sales Managers lead and motivate sales teams to achieve revenue goals.
Customer Success Manager
Customer Success Manager
Customer Success Managers ensure that customers are satisfied with a company's products and services.
Software Engineer
Software Engineer
Software Engineers develop, maintain, and improve software systems.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business.
Product Manager
Product Manager
Product Managers lead the development and launch of new products and features.
Analyst
Analyst
Analysts ensure that an organization can make informed decisions.
For more career information including salaries, visit:
OpenCourser.com/course/gj5vw8/understanding
Reading list
We've selected 11 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
Understanding Sentiment Analysis and Its Applications.
Provides a comprehensive introduction to natural language processing using the Python programming language. It covers the basics of natural language processing, as well as more advanced topics such as sentiment analysis and machine learning.
Provides a comprehensive overview of sentiment analysis and opinion mining. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of natural language processing, including a chapter on sentiment analysis. It valuable resource for anyone who wants to learn more about this field.
Save
Provides a practical guide to sentiment analysis. It covers the basics of sentiment analysis, as well as how to use it to solve real-world problems.
Save
Provides a short introduction to sentiment analysis. It valuable resource for anyone who wants to learn more about this field.
Save
Provides a comprehensive overview of machine learning for text, including a chapter on sentiment analysis. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of sentiment analysis. It covers the basics of sentiment analysis, as well as more advanced topics such as sentiment analysis in different languages and how to use sentiment analysis to track public opinion.
Provides a comprehensive overview of deep learning for natural language processing, including a chapter on sentiment analysis. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of sentiment analysis and opinion mining. It covers the basics of sentiment analysis and opinion mining, as well as more advanced topics such as sentiment analysis in different languages and how to use sentiment analysis and opinion mining to track public opinion.
Provides a comprehensive overview of natural language understanding, including a chapter on sentiment analysis. It valuable resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of text mining and analysis, including a chapter on sentiment analysis. It valuable resource for anyone who wants to learn more about this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gj5vw8/understanding
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser