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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/gj5vw8/understanding
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What's inside
Syllabus
Course Overview
Fundamentals, Techniques, and Key Concepts
Real World Application for Moving into Production
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save Understanding Sentiment Analysis and Its Applications to your list so you can find it easily later:
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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.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services.
Business Analyst
Business Analyst
Business Analysts help organizations improve their business processes and make better decisions.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to help organizations make informed decisions.
Product Manager
Product Manager
Product Managers lead the development and launch of new products and features.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business.
Software Engineer
Software Engineer
Software Engineers develop, maintain, and improve software systems.
Customer Success Manager
Customer Success Manager
Customer Success Managers ensure that customers are satisfied with a company's products and services.
Sales Manager
Sales Manager
Sales Managers lead and motivate sales teams to achieve revenue goals.
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
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Time
39 hours
Level
Introductory
Via
Pluralsight
Instructor
Allen O'Neill
Language
English
Good to know
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
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