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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/nexh7x/introduction
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What's inside
Syllabus
Project Overview
By the end of this 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.
This guided project is for beginners interested in quantitative text analysis in R. It assumes no knowledge of textual analysis and focuses on exploring textual data (US Presidential Concession Speeches). Users should have a basic understanding of the statistical programming language R.
Good to know
Good to know
Know what's good ,
what to watch for , 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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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Read 'Text Mining with R' by Julia Silge and David Robinson
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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:
Business 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.
Marketing 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.
Data 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.
Research Analyst
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.
Database Administrator
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.
Data Scientist
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.
Information Security Analyst
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.
Data Engineer
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.
Quantitative 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.
Cloud 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.
Network Engineer
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.
Web Developer
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.
Machine Learning Engineer
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.
Statistician
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.
Software 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.
For more career information including salaries, visit:
OpenCourser.com/course/nexh7x/introduction
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.
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.
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.
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.
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.
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.
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.
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.
Save
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.
Save
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nexh7x/introduction
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Nicole Baerg
Language
English
Good to know
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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