Building Sentiment Analysis Systems in Python from Pluralsight

Sentiment Analysis has become increasingly important as more opinions are expressed online, in unstructured form. This course covers rule-based and ML-based approaches to extracting sentiment from opinions, including VADER, Sentiwordnet, and more.

Online opinions are becoming ubiquitous - more people are expressing their views online than ever before. As a result, extracting sentiment information from these opinions is becoming very important. In this course, Building Sentiment Analysis Systems in Python, you will learn the fundamentals of building a system to do so in Python. First, you will learn the differences between ML- and rule-based approaches, and how to use VADER, Sentiwordnet, and Naive Bayes classifiers. Next, you will build three sentiment analyzers, and use them to classify a corpus of movie reviews made available by Cornell. Finally, you will gain a conceptual understanding of Support Vector Machines, and why Naive Bayes is usually a better choice. When you're finished with this course, you will have a clear understanding of how to extract sentiment from a body of opinions, and of the design choices and trade-offs involved.

What's inside

Syllabus

Course Overview

Identifying Applications of Sentiment Analysis

Solving Sentiment Analysis with a Rule-based Approach

Implementing Sentiment Analysis with a Rule-based Approach

Solving Sentiment Analysis with an ML Based Approach

Implementing Sentiment Analysis with an ML Based Approach

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a strong theoretical and practical foundation for anyone seeking to extract sentiment from text data in Python

Uses a wide variety of approaches and tools, including rule-based, machine learning, and natural language processing techniques

Is perfect for those who want to build their own sentiment analysis systems in Python or want to integrate sentiment analysis into their existing applications

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 Building Sentiment Analysis Systems in Python with these activities:

Review Sentiment Analysis Techniques

Show steps

Refresh your understanding of the fundamental concepts of sentiment analysis, covering both rule-based and ML-based techniques.

Browse courses on Sentiment Analysis

Show steps

Re-read course materials on sentiment analysis techniques.
Complete practice exercises on sentiment analysis.
Review online tutorials on sentiment analysis.

Attend Sentiment Analysis Meetups and Conferences

Show steps

Connect with professionals in the field of sentiment analysis at meetups and conferences, gaining insights into current trends and applications.

Browse courses on Networking

Show steps

Identify relevant sentiment analysis meetups and conferences.
Attend these events and actively participate in discussions.
Network with speakers, attendees, and industry experts.

Contribute to Open-Source Sentiment Analysis Projects

Show steps

Enhance your knowledge and practical skills by contributing to open-source sentiment analysis projects, gaining exposure to real-world codebases and best practices.

Browse courses on Community Involvement

Show steps

Identify open-source sentiment analysis projects that align with your interests.
Review the documentation and codebase.
Identify areas where you can contribute.
Submit pull requests with your contributions.

One other activity

Expand to see all activities and additional details

Show all four activities

Build a Sentiment Analysis Web Application

Show steps

Challenge yourself by building a full-stack web application that incorporates sentiment analysis functionality, solidifying your understanding of the practical applications of sentiment analysis.

Browse courses on Web Application Development

Show steps

Design the architecture and user interface for your web application.
Develop the back-end logic for sentiment analysis.
Create the front-end user interface.
Test and deploy your web application.

Career center

Learners who complete Building Sentiment Analysis Systems in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of data science tools and techniques to extract meaningful insights from data. This course can help Data Scientists build a foundation in sentiment analysis, which is a valuable skill for understanding customer feedback and making informed data-driven decisions.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers use their knowledge of machine learning principles and practices to develop and deploy machine learning models. This course can help Machine Learning Engineers build a foundation in sentiment analysis, which is a valuable skill for developing machine learning models that can understand customer feedback and make accurate predictions.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use their knowledge of business analysis tools and techniques to analyze business processes and make recommendations for improvement. This course can help Business Analysts build a foundation in sentiment analysis, which is a valuable skill for understanding customer feedback and making informed business decisions.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers use their knowledge of software engineering principles and practices to design, develop, and deploy software systems. This course can help Software Engineers build a foundation in sentiment analysis, which is a valuable skill for developing software systems that can understand customer feedback and make accurate predictions.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts use their knowledge of data analysis tools and techniques to extract meaningful insights from data. This course can help Data Analysts build a foundation in sentiment analysis, which is a valuable skill for understanding customer feedback and making informed business decisions.

See salaries and explore the career path for Data Analyst

Database Administrator

Database Administrators use their knowledge of database administration principles and practices to design, develop, and deploy database systems. This course can help Database Administrators build a foundation in sentiment analysis, which is a valuable skill for developing database systems that can store and manage customer feedback data.

See salaries and explore the career path for Database Administrator

Web Developer

Web Developers use their knowledge of web development principles and practices to design, develop, and deploy websites and web applications. This course can help Web Developers build a foundation in sentiment analysis, which is a valuable skill for developing websites and web applications that can understand customer feedback and provide personalized experiences.

See salaries and explore the career path for Web Developer

Product Manager

Product Managers use their knowledge of product management principles and practices to manage the development and launch of new products and services. This course can help Product Managers build a foundation in sentiment analysis, which is a valuable skill for understanding customer feedback and developing successful products and services.

See salaries and explore the career path for Product Manager

Public Relations Specialist

Public Relations Specialists use their knowledge of public relations principles and practices to manage the public image of organizations and individuals. This course can help Public Relations Specialists build a foundation in sentiment analysis, which is a valuable skill for understanding public opinion and developing effective public relations campaigns.

See salaries and explore the career path for Public Relations Specialist

Data Engineer

Data Engineers use their knowledge of data engineering principles and practices to design, develop, and deploy data pipelines. This course can help Data Engineers build a foundation in sentiment analysis, which is a valuable skill for developing data pipelines that can extract meaningful insights from customer feedback.

See salaries and explore the career path for Data Engineer

Customer Success Manager

Customer Success Managers use their knowledge of customer relationship management principles and practices to manage customer relationships and ensure customer satisfaction. This course can help Customer Success Managers build a foundation in sentiment analysis, which is a valuable skill for understanding customer feedback and developing effective customer success strategies.

See salaries and explore the career path for Customer Success Manager

UX Researcher

UX Researchers use their knowledge of user experience research methods and techniques to conduct research on user experience and make recommendations for improvement. This course can help UX Researchers build a foundation in sentiment analysis, which is a valuable skill for understanding user feedback and developing effective UX designs.

See salaries and explore the career path for UX Researcher

Market Researcher

Market Researchers use their knowledge of research methods and techniques to gather and interpret data about markets and customers. This course can help Market Researchers build a foundation in sentiment analysis, which is a valuable skill for understanding customer feedback and making informed marketing decisions.

See salaries and explore the career path for Market Researcher

Social Media Manager

Social Media Managers use their knowledge of social media platforms and marketing principles to manage social media accounts for organizations and individuals. This course can help Social Media Managers build a foundation in sentiment analysis, which is a valuable skill for understanding customer feedback and developing effective social media strategies.

See salaries and explore the career path for Social Media Manager

Natural Language Processing Engineer

Natural Language Processing Engineers use their knowledge of natural language processing principles and practices to develop and deploy natural language processing models. This course can help Natural Language Processing Engineers build a foundation in sentiment analysis, which is a valuable skill for developing natural language processing models that can understand customer feedback and generate natural language text.

See salaries and explore the career path for Natural Language Processing Engineer