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Understanding Sentiment Analysis and Its Applications

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.

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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.

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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

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

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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 Scientists apply their programming knowledge and statistical skills to solve real-world problems.
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve business problems.
Data Engineer
Data Engineers build and maintain the infrastructure to support data analysis and machine learning.
Data Analyst
Data Analysts analyze and interpret data to help businesses make better decisions.
Research Analyst
Research Analysts study market and industry trends to provide insights and recommendations to businesses.
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services.
Business Analyst
Business Analysts help organizations improve their business processes and make better decisions.
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations.
Risk Analyst
Risk Analysts identify and assess risks to help organizations make informed decisions.
Product Manager
Product Managers lead the development and launch of new products and features.
Operations Manager
Operations Managers oversee the day-to-day operations of a business.
Software Engineer
Software Engineers develop, maintain, and improve software systems.
Customer Success Manager
Customer Success Managers ensure that customers are satisfied with a company's products and services.
Sales Manager
Sales Managers lead and motivate sales teams to achieve revenue goals.
Analyst
Analysts ensure that an organization can make informed decisions.

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.
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.
Provides a short introduction to sentiment analysis. It valuable resource for anyone who wants to learn more about this field.
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.

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