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Sentiment Analysis with Deep Learning using BERT

Ari Anastassiou

In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge.

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In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge.

Note: 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.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Introduces skills, knowledge, and tools necessary for sentiment analysis with Deep Learning
Utilizes PyTorch BERT model to delve into the topic of multi-class classification
Provides guidance on optimizing and scheduling models to optimize training performance
Trains students on designing training and evaluation loops to monitor model performance during training
Enables students to create sentiment analysis models leveraging BERT's immense language knowledge
Course duration is relatively short, allowing for quick completion

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Reviews summary

Practical bert sentiment analysis project

According to students, this course offers a highly practical and effective guide to sentiment analysis using BERT and PyTorch. Learners consistently praise its hands-on, project-based approach, which helps solidify understanding of fine-tuning deep learning models for multi-class classification. While many find it a useful and concise experience, the course has a fast pace and assumes prior knowledge of PyTorch and deep learning fundamentals, making it less suitable for absolute beginners. Additionally, some learners reported technical issues with the lab environment, particularly outside the North America region. Despite these points, it is generally considered a valuable practical stepping stone for applying BERT.
A fast-paced project, ideal for quick skill acquisition.
"A concise and practical guide... My only minor gripe is the speed; it's quite fast-paced."
"The content itself is good, but it rushes through several critical PyTorch concepts."
"It's a fast-paced project that gets straight to the point, which I appreciate."
Offers a hands-on approach to applying BERT for sentiment analysis.
"This project was exactly what I needed to get my hands dirty with BERT fine-tuning."
"Absolutely brilliant for practical application. I was able to replicate the results and adapt the code for my own dataset."
"I learned how to apply BERT to a real-world sentiment task in just under 2 hours. Super useful!"
Varied experiences with the coding environment, some facing issues.
"I had issues with the lab environment repeatedly. It was frustrating and made the 2-hour project stretch much longer."
"The regional restriction mentioned in the description is real. I struggled constantly with the lab environment outside North America."
"The course environment worked seamlessly for me, which was a relief. This was a concern from older reviews."
Requires prior knowledge of PyTorch and deep learning concepts.
"It assumes you know PyTorch already, so if you're a complete beginner, it might be a bit fast."
"I felt lost from the start as someone new to deep learning. This is NOT for beginners."
"This course is for implementation, not foundational understanding, best for those who know the 'why'."

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 Sentiment Analysis with Deep Learning using BERT with these activities:
Review PyTorch
Begin preparing for this course by reviewing PyTorch, the deep learning library focus of this course. This review will solidify your foundation to prepare you for success when the coursework begins in earnest.
Browse courses on PyTorch
Show steps
  • Read and understand the PyTorch documentation
  • Complete PyTorch tutorials for beginners
  • Review PyTorch examples on GitHub
Find a Mentor in the Industry
Accelerate your learning and career growth by finding a mentor in the NLP industry. A mentor can provide you with valuable insights, guidance, and support.
Browse courses on Mentoring
Show steps
  • Identify potential mentors through LinkedIn or industry events
  • Reach out and introduce yourself
Follow Hugging Face Tutorials
Supplement your coursework by completing tutorials from Hugging Face, the company that developed Transformers. These tutorials will provide you with valuable hands-on experience with BERT.
Browse courses on Hugging Face
Show steps
  • Complete the Hugging Face BERT tutorial
  • Explore other Hugging Face tutorials related to BERT
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group
Enhance your learning by joining a study group with other students enrolled in this course. Collaborating with peers will provide you with diverse perspectives and opportunities to clarify concepts.
Show steps
  • Find a study group on online forums
  • Organize regular study sessions
  • Discuss course materials and share insights
Complete Coding Exercises
Reinforce your understanding of BERT and natural language processing by completing coding exercises. These exercises will test your skills and help you identify areas where you need additional practice.
Browse courses on Python
Show steps
  • Solve coding exercises on Kaggle
  • Complete coding challenges on HackerRank
Participate in a Kaggle Competition
Challenge yourself and showcase your skills by participating in a Kaggle competition related to sentiment analysis or BERT. This will provide you with an opportunity to apply your knowledge and potentially earn recognition for your work.
Browse courses on Kaggle
Show steps
  • Identify and join a relevant competition
  • Develop and submit your solution
  • Analyze your results and learn from feedback
Build a Sentiment Analysis App
Deepen your understanding of sentiment analysis by building a practical application. This project will challenge you to apply the concepts you learn in class and develop a valuable skill.
Browse courses on BERT
Show steps
  • Design and plan the app's functionality
  • Develop the app using a suitable framework
  • Deploy the app and evaluate its performance
Mentor Junior Students
Reinforce your understanding of BERT and NLP by mentoring junior students. This will provide you with an opportunity to clarify concepts, share your knowledge, and develop your leadership skills.
Browse courses on Mentoring
Show steps
  • Identify and connect with junior students seeking mentorship
  • Establish regular mentoring sessions
  • Provide guidance and support in understanding BERT and NLP

Career center

Learners who complete Sentiment Analysis with Deep Learning using BERT will develop knowledge and skills that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineers develop and maintain systems that can understand and generate human language. Sentiment analysis is a key application of natural language processing. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Natural Language Processing Engineers build and improve sentiment analysis systems.
See salaries and explore the career path for Natural Language Processing Engineer
Data Analyst
Data Analysts analyze data to extract meaningful insights. These insights help businesses make more informed decisions. Sentiment analysis is a type of data analysis. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Data Analysts build and improve sentiment analysis models.
See salaries and explore the career path for Data Analyst
Business Analyst
Business Analysts analyze business processes to identify areas for improvement. Sentiment analysis can be used to understand customer feedback and improve business processes. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Business Analysts build and improve sentiment analysis models to support business decisions.
See salaries and explore the career path for Business Analyst
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. Sentiment analysis is a common application of machine learning. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. These skills are essential for Machine Learning Engineers working on sentiment analysis projects.
See salaries and explore the career path for Machine Learning Engineer
User Experience Designer
User Experience Designers design and evaluate user experiences for products and services. Sentiment analysis can be used to understand user feedback and improve user experiences. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help User Experience Designers build and improve sentiment analysis models to support user experience design decisions.
See salaries and explore the career path for User Experience Designer
Software Engineer
Software Engineers design, develop, and maintain software systems. Sentiment analysis is a common application of software engineering. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. These skills can help Software Engineers build and improve sentiment analysis software systems.
See salaries and explore the career path for Software Engineer
Marketing Manager
Marketing Managers develop and execute marketing campaigns. Sentiment analysis can be used to understand customer feedback and improve marketing campaigns. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Marketing Managers build and improve sentiment analysis models to support marketing decisions.
See salaries and explore the career path for Marketing Manager
Product Manager
Product Managers develop and manage products. Sentiment analysis can be used to understand customer feedback and improve products. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Product Managers build and improve sentiment analysis models to support product decisions.
See salaries and explore the career path for Product Manager
Customer Success Manager
Customer Success Managers help customers achieve success with a company's products or services. Sentiment analysis can be used to understand customer feedback and improve customer success efforts. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Customer Success Managers build and improve sentiment analysis models to support customer success initiatives.
See salaries and explore the career path for Customer Success Manager
Sales Manager
Sales Managers lead and manage sales teams. Sentiment analysis can be used to understand customer feedback and improve sales strategies. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Sales Managers build and improve sentiment analysis models to support sales decisions.
See salaries and explore the career path for Sales Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. Sentiment analysis can be used to understand customer feedback and improve operations. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Operations Managers build and improve sentiment analysis models to support operational decisions.
See salaries and explore the career path for Operations Manager
Project Manager
Project Managers plan and execute projects. Sentiment analysis can be used to understand customer feedback and improve project outcomes. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Project Managers build and improve sentiment analysis models to support project decisions.
See salaries and explore the career path for Project Manager
Technical Writer
Technical Writers create and maintain technical documentation. Sentiment analysis can be used to analyze customer feedback on technical documentation. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Technical Writers build and improve sentiment analysis models to support documentation decisions.
See salaries and explore the career path for Technical Writer
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate software to ensure it meets quality standards. Sentiment analysis can be used to analyze customer feedback on software quality. The course, Sentiment Analysis with Deep Learning using BERT, provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. This course can help Quality Assurance Analysts build and improve sentiment analysis models to support software quality assurance efforts.
See salaries and explore the career path for Quality Assurance Analyst
Data Scientist
Data Scientists analyze data with a variety of techniques to extract meaningful insights. These data-driven insights help businesses make more informed decisions. The course, Sentiment Analysis with Deep Learning using BERT, may be useful for this career as it provides a foundation in sentiment analysis techniques and the application of deep learning models like BERT. These techniques are commonly used by Data Scientists for sentiment analysis in various industries.
See salaries and explore the career path for Data Scientist

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 Sentiment Analysis with Deep Learning using BERT.
Cover image
Natural Language Processing with PyTorch
Save
Focuses on practical NLP tasks using PyTorch, including sentiment analysis. It provides step-by-step instructions and code examples, making it a useful resource for applying BERT in real-world scenarios.
Natural Language Processing with PyTorch: Build...
Paperback
$$$
(Deutsch) Natural Language Processing mit PyTorch:...
Kindle Edition
$$
(中文) Based on natural language processing...
Paperback
$$
Cover image
An Introduction to Statistical Learning
Save
Provides a solid foundation in statistical learning, which is essential for understanding the statistical principles behind NLP models like BERT. It covers topics such as regression, classification, and dimensionality reduction.
An Introduction to Statistical Learning: with...
Hardcover
$$$
Cover image
Cyberformalism
Save
This classic textbook provides a comprehensive overview of speech and language processing, including NLP. It covers a wide range of topics, including sentiment analysis, and offers a solid foundation for understanding the field.
Cyberformalism: Histories of Linguistic Forms in...
Hardcover
$$
Cyberformalism: Histories of Linguistic Forms in...
Kindle Edition
$$
Cover image
Mental diseases; a public health problem
Save
Provides a more theoretical perspective on NLP, covering topics such as semantics, pragmatics, and discourse. While it does not focus on BERT or deep learning, it offers a valuable foundation for understanding the linguistic principles behind NLP.
Mental diseases; a public health problem by James...
Kindle Edition
$
Foundations of Statistical Natural Language...
Save
Provides a comprehensive introduction to statistical NLP, covering topics such as language modeling, machine translation, and information retrieval. While it does not cover BERT specifically, it offers a solid foundation for understanding the statistical techniques used in NLP.
Foundations of Statistical Natural Language...
Paperback
Check
Foundations of Statistical Natural Language...
Kindle Edition
Check
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning, including its theoretical foundations and applications. While it does not cover NLP specifically, it offers a valuable foundation for understanding the deep learning techniques used in BERT.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Artificial Intelligence
Save
This classic textbook provides a comprehensive overview of artificial intelligence, including NLP. While it does not cover BERT specifically, it offers a valuable foundation for understanding the broader context of NLP within AI.
Artificial Intelligence: A Modern Approach, Global...
Paperback
$$$
Artificial Intelligence: A Modern Approach (Pearson...
Hardcover
$$$$
Human Compatible: Artificial Intelligence and the...
Paperback
$$
Pearson Artificial Intelligence: A Modern Approach,...
Paperback
$$
Artificial Intelligence: A Modern Approach
Hardcover
$$$$
Artificial Intelligence: A Modern Approach
Hardcover
$$
Artificial Intelligence: A Modern Approach
Hardcover
$$$
Do the Right Thing: Studies in Limited Rationality...
Paperback
$$
(中文) Artificial Intelligence: A Modern Approach (4th...
Hardcover
$$$
Artificial Intelligence: A Modern Approach (Pearson...
Kindle Edition
$$$
Human Compatible: Artificial Intelligence and the...
Kindle Edition
$
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive overview of statistical learning, covering topics such as regression, classification, and dimensionality reduction. While it does not cover NLP specifically, it offers a valuable foundation for understanding the statistical techniques used in NLP.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised and unsupervised learning. While it does not cover NLP or BERT specifically, it offers a valuable foundation for understanding the broader context of NLP within machine learning.
Deep Learning: Foundations and Concepts
Hardcover
$$$

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Intermediate
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Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Introduces skills, knowledge, and tools necessary for sentiment analysis with Deep Learning
Utilizes PyTorch BERT model to delve into the topic of multi-class classification
Provides guidance on optimizing and scheduling models to optimize training performance
Trains students on designing training and evaluation loops to monitor model performance during training
Enables students to create sentiment analysis models leveraging BERT's immense language knowledge
Course duration is relatively short, allowing for quick completion
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