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Introduction to Sentiment Analysis in R with quanteda

Nicole Baerg

In this guided project, you will learn how to import textual data stored in raw text files into R, turn these files into a corpus (a collection of textual documents), and tokenize the text all using the R software package quanteda. You will then learn how to check for words with positive or negative sentiment within the text, and how to plot the proportion of use for these words over time, while stratifying by a third variable. You will also learn how to carry out a targeted sentiment analysis by looking for words with a positive or negative sentiment that are adjacent to relevant keywords or phrases, and how to compare the results of a targeted sentiment analysis with the results of a generic analysis.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Introduces beginners to essential textual analysis methods and tools
Uses the widely-used R software and quanteda package
Focuses on exploring and analyzing US Presidential Concession Speeches
Assumes a basic understanding of the R statistical programming language

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Reviews summary

Practical sentiment analysis with r and quanteda

Learners say this course provides an excellent, hands-on introduction to sentiment analysis using the powerful quanteda R package. Many found the guided project format highly effective for learning practical skills. It's particularly well-suited for those with basic R knowledge looking to apply text analysis techniques. While generally very positively received, some students noted that a solid grasp of R fundamentals is crucial, as the course dives directly into application rather than basic R syntax or environment setup. Overall, it's praised for its clarity, conciseness, and immediate applicability.
The project format is concise, efficient, and facilitates hands-on learning.
"The guided project format was fantastic, allowing me to learn by doing without getting overwhelmed."
"It was a short, to-the-point project that delivered exactly what it promised in a short time."
"I appreciate the bite-sized, practical approach to learning a specific skill quickly."
Excellent introduction for those with basic R but new to text analysis.
"This course is an excellent starting point for anyone with basic R knowledge looking to dive into text analysis."
"I found it perfect for a beginner like me who wanted to understand sentiment analysis in R."
"A great project to get me started with using R for text analysis, assuming I had some prior R experience."
Instructor's explanations are consistently clear and easy to follow.
"The instructor did a great job explaining the concepts clearly and guiding me step-by-step through the process."
"The explanations were very concise and simple to understand, making complex topics approachable."
"I appreciated the clear guidance provided, which helped me grasp new concepts effectively."
Teaches direct application of the quanteda R package for text analysis.
"The hands-on coding and projects are the strongest part of the course for me, providing practical skills with the quanteda package."
"I learned how to use practical tools and strategies that I could apply immediately to my work using quanteda."
"I found the course very practical for using the quanteda package, covering tokenization, corpus creation, and sentiment analysis."
Requires a solid understanding of R fundamentals beyond basic syntax.
"While it says basic R, I found it helped to have more than just basic knowledge to follow along smoothly."
"I struggled a bit with environment setup and package installations, which wasn't fully covered in the project."
"Some parts assumed more familiarity with R than I initially expected, making it a bit challenging."

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 Introduction to Sentiment Analysis in R with quanteda with these activities:
Read 'Text Mining with R' by Julia Silge and David Robinson
Supplement your knowledge with a comprehensive book on text mining with R, providing additional depth and insights beyond the scope of the course.
View Text Mining with R: A Tidy Approach on Amazon
Show steps
  • Read and understand the chapters relevant to sentiment analysis
  • Apply the techniques discussed in the book to practical examples
Analyze sentiment with quanteda
Practice working with quanteda to assess the sentiment of various textual data, solidifying your understanding of the techniques covered in the course.
Browse courses on Sentiment Analysis
Show steps
  • Load and tokenize the data using quanteda
  • Identify words with positive and negative sentiment using built-in dictionaries
  • Visualize the proportion of positive and negative words over time
  • Carry out targeted sentiment analysis by searching for words with specific sentiments adjacent to relevant keywords
Expand knowledge on text analysis with quanteda
Deepen your understanding of text analysis techniques by exploring additional tutorials and resources on quanteda, complementing the concepts covered in the course.
Browse courses on quanteda
Show steps
  • Explore the quanteda documentation for advanced functions and techniques
  • Follow online tutorials on specific text analysis tasks using quanteda
  • Attend workshops or webinars on quanteda
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in a text analysis competition
Challenge yourself and test your skills by participating in a text analysis competition, applying your knowledge in a competitive environment and potentially winning recognition.
Browse courses on Kaggle
Show steps
  • Find a relevant competition on platforms like Kaggle
  • Study the competition details and data provided
  • Develop and implement your text analysis approach
  • Submit your results and analyze your performance
Apply quanteda to analyze a specific dataset
Reinforce your understanding by applying quanteda to analyze a dataset of your choice, demonstrating your ability to use the techniques in a practical context.
Browse courses on Sentiment Analysis
Show steps
  • Choose a dataset that aligns with your interests or research
  • Apply the techniques covered in the course to analyze the dataset
  • Interpret the results and draw meaningful conclusions
  • Write a report or presentation summarizing your findings
Develop a blog post or article on sentiment analysis with quanteda
Solidify your knowledge by creating a blog post or article that explains the concepts of sentiment analysis using quanteda, reinforcing your understanding and potentially benefiting others.
Browse courses on Sentiment Analysis
Show steps
  • Choose a specific aspect of sentiment analysis to focus on
  • Research and gather information from relevant sources
  • Write a clear and informative article explaining the concepts
  • Include examples and demonstrations using quanteda
  • Publish your article on a relevant platform
Develop a dashboard for visualizing sentiment analysis results
Extend your knowledge by creating a dashboard that visualizes the results of your sentiment analysis, enabling you to effectively communicate insights and monitor trends.
Browse courses on Data Visualization
Show steps
  • Design the dashboard layout and visualizations
  • Use R or other visualization tools to create interactive charts and graphs
  • Integrate the dashboard with your sentiment analysis code
  • Deploy the dashboard and monitor its usage

Career center

Learners who complete Introduction to Sentiment Analysis in R with quanteda will develop knowledge and skills that may be useful to these careers:
Research Analyst
Research Analysts conduct studies and analyze data to solve business problems and improve decision making. They use statistical analysis and qualitative research to understand consumer behavior, market trends, and other factors that can impact a company's success. This course in Introduction to Sentiment Analysis in R with quanteda can be useful for aspiring Research Analysts as it provides a foundation in data analysis and text mining, which are essential skills for extracting insights from large datasets.
See salaries and explore the career path for Research Analyst
Data Analyst
Data Analysts use data and statistical techniques to solve business problems and extract valuable insights. They work with large datasets to identify trends, patterns, and relationships that can help companies make informed decisions. This course in Introduction to Sentiment Analysis in R with quanteda can be useful for aspiring Data Analysts as it provides a foundation in data analysis and text mining, which are essential skills for working with large datasets.
See salaries and explore the career path for Data Analyst
Marketing Analyst
Marketing Analysts use data and statistical techniques to understand consumer behavior and develop marketing strategies. They work with large datasets to identify trends, patterns, and relationships that can help companies reach their target audience and achieve their marketing goals. This course in Introduction to Sentiment Analysis in R with quanteda can be useful for aspiring Marketing Analysts as it provides a foundation in data analysis and text mining, which are essential skills for working with large datasets.
See salaries and explore the career path for Marketing Analyst
Business Analyst
Business Analysts use data and statistical techniques to understand business problems and improve decision making. They work with stakeholders to identify needs, gather data, and analyze results to make recommendations for improvements. This course in Introduction to Sentiment Analysis in R with quanteda can be useful for aspiring Business Analysts as it provides a foundation in data analysis and text mining, which are essential skills for working with large datasets.
See salaries and explore the career path for Business Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use data to identify trends, patterns, and relationships that can help investors make informed decisions. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Quantitative Analysts as it provides a foundation in data analysis and text mining, which can be helpful for understanding financial data and making investment decisions.
See salaries and explore the career path for Quantitative Analyst
Statistician
Statisticians use data and statistical techniques to solve problems in a variety of fields, including science, medicine, and business. They use data to identify trends, patterns, and relationships that can help researchers and decision makers make informed decisions. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Statisticians as it provides a foundation in data analysis and text mining, which are essential skills for working with large datasets.
See salaries and explore the career path for Statistician
Data Scientist
Data Scientists use data and statistical techniques to solve problems in a variety of fields, including science, medicine, and business. They use data to identify trends, patterns, and relationships that can help researchers and decision makers make informed decisions. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Data Scientists as it provides a foundation in data analysis and text mining, which are essential skills for working with large datasets.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve problems in a variety of fields, including science, medicine, and business. They use data to train models that can make predictions or classifications. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Machine Learning Engineers as it provides a foundation in data analysis and text mining, which are essential skills for working with large datasets.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use programming languages to create software that meets the needs of users. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Software Engineers as it provides a foundation in data analysis and text mining, which can be helpful for developing software applications that work with large datasets.
See salaries and explore the career path for Software Engineer
Web Developer
Web Developers design and develop websites. They use programming languages to create websites that are visually appealing and user-friendly. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Web Developers as it provides a foundation in data analysis and text mining, which can be helpful for developing websites that are user-friendly and meet the needs of users.
See salaries and explore the career path for Web Developer
Data Engineer
Data Engineers design and develop data pipelines to store and process large datasets. They use data engineering tools and techniques to ensure that data is clean, accurate, and accessible. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Data Engineers as it provides a foundation in data analysis and text mining, which are essential skills for working with large datasets.
See salaries and explore the career path for Data Engineer
Database Administrator
Database Administrators design and maintain databases. They use database management systems to ensure that data is stored and organized in a way that is efficient and accessible. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Database Administrators as it provides a foundation in data analysis and text mining, which can be helpful for working with large datasets.
See salaries and explore the career path for Database Administrator
Information Security Analyst
Information Security Analysts design and implement security measures to protect data and systems from unauthorized access or attack. They use security tools and techniques to identify and mitigate security risks. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Information Security Analysts as it provides a foundation in data analysis and text mining, which can be helpful for identifying and mitigating security risks.
See salaries and explore the career path for Information Security Analyst
Network Engineer
Network Engineers design and maintain computer networks. They use networking hardware and software to ensure that networks are reliable, secure, and efficient. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Network Engineers as it provides a foundation in data analysis and text mining, which can be helpful for identifying and mitigating network problems.
See salaries and explore the career path for Network Engineer
Cloud Engineer
Cloud Engineers design and maintain cloud computing systems. They use cloud computing platforms to provide scalable and reliable cloud services. This course in Introduction to Sentiment Analysis in R with quanteda may be useful for aspiring Cloud Engineers as it provides a foundation in data analysis and text mining, which can be helpful for developing and maintaining cloud computing systems.
See salaries and explore the career path for Cloud Engineer

Reading list

We've selected ten 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 Introduction to Sentiment Analysis in R with quanteda.
Cover image
Foundations of Statistical Natural Language...
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Provides a comprehensive overview of natural language processing, covering topics such as text preprocessing, machine learning algorithms, and applications. It valuable reference for those looking to gain a deeper understanding of the theoretical foundations and practical applications of natural language processing.
Foundations of Statistical Natural Language...
Hardcover
$$$
Foundations of Statistical Natural Language...
Kindle Edition
$$$
Cover image
Cross-Lingual Word Embeddings
Save
Provides a comprehensive overview of natural language processing and machine learning for NLP, covering topics such as language models, machine learning algorithms, and applications. It valuable reference for those looking to gain a deeper understanding of the theoretical foundations and practical applications of natural language processing.
Cross-Lingual Word Embeddings (Synthesis Lectures...
Paperback
$$
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Speech and Language Processing. An Introduction to...
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Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language processing, and machine learning algorithms. It valuable reference for those looking to gain a deeper understanding of the theoretical foundations and practical applications of speech and language processing.
Speech and Language Processing An Introduction to...
Paperback
$$
Foundations of Statistical Natural Language...
Save
Provides a comprehensive overview of the statistical foundations of natural language processing, covering topics such as probability theory, machine learning, and applications. It valuable reference for those looking to gain a deeper understanding of the theoretical foundations of natural language processing.
Foundations of Statistical Natural Language...
Paperback
Check
Foundations of Statistical Natural Language...
Kindle Edition
Check
Cover image
Text Mining
Save
Provides a comprehensive overview of deep learning for natural language processing, covering topics such as deep learning architectures, machine learning algorithms, and applications. It valuable reference for those looking to gain a deeper understanding of the theoretical foundations and practical applications of deep learning for natural language processing.
Text Mining: Predictive Methods for Analyzing...
Hardcover
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Text Mining: Predictive Methods for Analyzing...
Kindle Edition
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Lifelong and Continual Learning Dialogue Systems
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Provides a comprehensive overview of sentiment analysis and opinion mining, covering topics such as sentiment analysis techniques, machine learning algorithms, and applications. It valuable reference for those looking to gain a deeper understanding of the theoretical foundations and practical applications of sentiment analysis.
Lifelong and Continual Learning Dialogue Systems...
Hardcover
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Cover image
The Elements of Statistical Learning
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Provides a comprehensive overview of statistical learning, covering topics such as linear regression, classification, and machine learning algorithms. It valuable reference for those looking to gain a deeper understanding of the theoretical foundations and practical applications of statistical learning.
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
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Cover image
Creative Convergence
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Provides a comprehensive overview of machine learning for text, covering topics such as language models, machine learning algorithms, and applications. It valuable reference for those looking to gain a deeper understanding of the theoretical foundations and practical applications of machine learning for text.
Creative Convergence: The AI Renaissance in Art and...
Hardcover
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Creative Convergence: The AI Renaissance in Art and...
Kindle Edition
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Cover image
Text Mining with R
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Provides an accessible introduction to text analysis in R, covering topics such as data preprocessing, text mining, and visualization. It useful resource for those with little to no prior experience in text analysis.
Text Mining with R
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Text Mining with R
Kindle Edition
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Cover image
Introduction to Natural Language Processing
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Provides an accessible introduction to natural language processing, covering topics such as language models, machine learning algorithms, and applications. It useful resource for those with little to no prior experience in natural language processing.
Introduction to Natural Language Processing...
Hardcover
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Introduction to Natural Language Processing...
Kindle Edition
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Course image
Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Nicole Baerg
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Introduces beginners to essential textual analysis methods and tools
Uses the widely-used R software and quanteda package
Focuses on exploring and analyzing US Presidential Concession Speeches
Assumes a basic understanding of the R statistical programming language
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