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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), reshape them into paragraphs from documents and tokenize the text all using the R software package quanteda. You will then learn how to classify the texts using the Naive Bayes algorithm.
Read more
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), reshape them into paragraphs from documents and tokenize the text all using the R software package quanteda. You will then learn how to classify the texts using the Naive Bayes algorithm.
Read more
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), reshape them into paragraphs from documents and tokenize the text all using the R software package quanteda. You will then learn how to classify the texts using the Naive Bayes algorithm.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/js7sdz/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), reshape them into paragraphs from documents and tokenize the text all using the R software package quanteda. You will then learn how to classify the texts using the Naive Bayes algorithm.
At the end of this project, among other things, you will have imported documents, reshaped texts from documents to paragraphs, turned your texts into a machine readable format, and classified presidential concession speeches by political party. You will also learn to assess the accuracy of the predictions. 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. By the end of the exercise, learners will know how to load textual data into R, summarize the data using descriptive quantities of interest, turn text into tokens, and do simple text classification.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops knowledge of importing textual data into R, turning texts into a machine-readable format, and classifying presidential concession speeches by political party
Builds a foundation for understanding quantitative text analysis in R
Teaches how to assess the accuracy of predictions made through the Naive Bayes algorithm
Assumes no knowledge of textual analysis, making it accessible to beginners
Requires a basic understanding of the R programming language
Focuses on exploring textual data specifically from US Presidential Concession Speeches
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Save Introduction to Text Classification in R with quanteda to your list so you can find it easily later:
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Reviews summary
Solid text classification course
According to students, this introductory Text Classification course uses R and quanteda. Students largely find the course to be well-structured.
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 Text Classification in R with quanteda with these
activities:
Review basic R programming concepts
Show steps
Prepare yourself for the course by reviewing the fundamentals of R programming, ensuring a solid foundation for understanding the upcoming material.
Browse courses on
R Programming
Show steps
-
Go over R syntax and data types
-
Refresh your knowledge of data manipulation functions
Understanding Natural Language Processing with Python and R
Show steps
Strengthen your foundation by reviewing this comprehensive resource on natural language processing techniques, complementing the course material and expanding your knowledge.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read the chapters related to text analysis
-
Work through the provided exercises
-
Apply the concepts to a small text analysis project
Compile a glossary of text analysis terms
Show steps
Enhance your understanding by creating a glossary of key text analysis terms, solidifying your grasp of the essential concepts and improving retention.
Show steps
-
Identify key terms from the course material and external resources
-
Define each term clearly and concisely
-
Organize the terms alphabetically or by category
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore the quanteda package
Show steps
Familiarize yourself with the basics of the quanteda package by following tutorials, allowing you to better comprehend the course material.
Browse courses on
quanteda
Show steps
-
Identify a beginner-friendly tutorial on the quanteda package
-
Follow the tutorial to import a text file
-
Create a corpus and reshape data using quanteda functions
-
Tokenize and lemmatize the text
Practice text analysis using the Naive Bayes algorithm
Show steps
Reinforce your understanding of the Naive Bayes algorithm by practicing text classification, solidifying the concepts covered in the course.
Browse courses on
Naive Bayes
Show steps
-
Find a dataset with labeled text data
-
Load the data into R and split it into training and testing sets
-
Train a Naive Bayes model on the training data
-
Evaluate the model's performance on the test data
Participate in a text analysis competition
Show steps
Challenge yourself and test your skills by participating in a text analysis competition, offering an opportunity to apply your knowledge in a practical setting.
Browse courses on
Competition
Show steps
-
Identify a relevant text analysis competition (e.g., Kaggle, DrivenData)
-
Gather a team or participate individually
-
Apply the concepts learned in the course to build a model
-
Submit your solution and analyze the results
Create a visualization of presidential concession speeches
Show steps
Enhance your comprehension of the data by creating a visualization of presidential concession speeches, reinforcing your knowledge and fostering critical analysis.
Browse courses on
Visualization
Show steps
-
Choose a visualization technique (e.g., word cloud, bar chart)
-
Extract the relevant data from the speeches
-
Create the visualization using a data visualization tool (e.g., ggplot2)
-
Analyze and interpret the visualization
Review basic R programming concepts
Show steps
Prepare yourself for the course by reviewing the fundamentals of R programming, ensuring a solid foundation for understanding the upcoming material.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Go over R syntax and data types
- Refresh your knowledge of data manipulation functions
Understanding Natural Language Processing with Python and R
Show steps
Strengthen your foundation by reviewing this comprehensive resource on natural language processing techniques, complementing the course material and expanding your knowledge.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters related to text analysis
- Work through the provided exercises
- Apply the concepts to a small text analysis project
Compile a glossary of text analysis terms
Show steps
Enhance your understanding by creating a glossary of key text analysis terms, solidifying your grasp of the essential concepts and improving retention.
Show steps
Show steps
Show steps
- Identify key terms from the course material and external resources
- Define each term clearly and concisely
- Organize the terms alphabetically or by category
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore the quanteda package
Show steps
Familiarize yourself with the basics of the quanteda package by following tutorials, allowing you to better comprehend the course material.
Browse courses on
quanteda
Show steps
Show steps
Show steps
- Identify a beginner-friendly tutorial on the quanteda package
- Follow the tutorial to import a text file
- Create a corpus and reshape data using quanteda functions
- Tokenize and lemmatize the text
Practice text analysis using the Naive Bayes algorithm
Show steps
Reinforce your understanding of the Naive Bayes algorithm by practicing text classification, solidifying the concepts covered in the course.
Browse courses on
Naive Bayes
Show steps
Show steps
Show steps
- Find a dataset with labeled text data
- Load the data into R and split it into training and testing sets
- Train a Naive Bayes model on the training data
- Evaluate the model's performance on the test data
Participate in a text analysis competition
Show steps
Challenge yourself and test your skills by participating in a text analysis competition, offering an opportunity to apply your knowledge in a practical setting.
Browse courses on
Competition
Show steps
Show steps
Show steps
- Identify a relevant text analysis competition (e.g., Kaggle, DrivenData)
- Gather a team or participate individually
- Apply the concepts learned in the course to build a model
- Submit your solution and analyze the results
Create a visualization of presidential concession speeches
Show steps
Enhance your comprehension of the data by creating a visualization of presidential concession speeches, reinforcing your knowledge and fostering critical analysis.
Browse courses on
Visualization
Show steps
Show steps
Show steps
- Choose a visualization technique (e.g., word cloud, bar chart)
- Extract the relevant data from the speeches
- Create the visualization using a data visualization tool (e.g., ggplot2)
- Analyze and interpret the visualization
Career center
Learners who complete Introduction to Text Classification in R with quanteda will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Researcher
Natural Language Processing Researcher
As a NLP Researcher, you will research and develop natural language processing algorithms and techniques. In addition to focusing on understanding spoken and written language, NLP researchers also work to enable computers to communicate with humans in a natural way. Using foundational knowledge in text analytics, such as that learned through the Introduction to Text Classification in R with quanteda course, you will be able to contribute to the development of new NLP technologies.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. Data Analysts may focus on statistical analysis, data mining, or machine learning, each of which has applications in text analytics. With the text classification skills learned in the Introduction to Text Classification in R with quanteda course, you will be positioned well to help make sense of the large amounts of data that organizations collect.
Machine Learning Engineer
Machine Learning Engineer
Building, deploying, and maintaining machine learning models are the core responsibilities of Machine Learning Engineers. Some Machine Learning Engineers focus on NLP, in which case they concentrate on developing models that can process and understand human language. The Introduction to Text Classification in R with quanteda course can be a valuable asset for those interested in working in this specialized area of machine learning.
Computational Linguist
Computational Linguist
Computational Linguists apply computational techniques to the study of language, helping to bridge the gap between computer science and linguistics. With a strong foundation in text analytics, such as that developed in the Introduction to Text Classification in R with quanteda course, Computational Linguists can pursue a wide range of research interests, such as natural language processing, machine translation, and speech recognition.
Information Architect
Information Architect
Information Architects design and organize information systems to make them easy to find and use. To do so, they need to have a strong understanding of how people interact with information, which is why many Information Architects have backgrounds in library science or linguistics. The Introduction to Text Classification in R with quanteda course could be useful for those who wish to specialize in organizing textual information.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how people interact with products and services in order to improve their usability. This often involves collecting and analyzing data on user behavior, which can include text data. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to collect and analyze this type of data.
Content Strategist
Content Strategist
Content Strategists help organizations create and manage content that is effective and engaging. This often involves understanding the needs of the target audience and developing content that is tailored to those needs. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to analyze and understand text data, which can be useful for developing content strategies.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and market trends. This often involves collecting and analyzing text data, such as social media data or customer reviews. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to collect and analyze this type of data.
Public Relations Specialist
Public Relations Specialist
Public Relations Specialists manage the public image of organizations and individuals. This often involves writing and distributing press releases, managing social media accounts, and responding to media inquiries. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to analyze and understand text data, which can be useful for developing public relations strategies.
Community Manager
Community Manager
Community Managers manage online communities, such as social media groups and forums. This often involves creating and curating content, moderating discussions, and responding to user inquiries. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to analyze and understand text data, which can be useful for developing community management strategies.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for software, hardware, and other technical products. This documentation can include user manuals, white papers, and technical reports. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to analyze and understand text data, which can be useful for developing technical documentation.
Knowledge Manager
Knowledge Manager
Knowledge Managers create and manage knowledge repositories for organizations. This often involves collecting, organizing, and disseminating information from a variety of sources. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to analyze and understand text data, which can be useful for developing knowledge management strategies.
Digital Marketing Specialist
Digital Marketing Specialist
Digital Marketing Specialists develop and implement digital marketing campaigns for businesses. This often involves creating and managing content, running social media ads, and tracking website traffic. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to analyze and understand text data, which can be useful for developing digital marketing strategies.
Business Analyst
Business Analyst
Business Analysts analyze business needs and develop solutions to improve efficiency and productivity. This often involves collecting and analyzing data, including text data. The Introduction to Text Classification in R with quanteda course can be a good way to develop the skills needed to collect and analyze this type of data.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This often involves working with text data, such as source code or user documentation. The Introduction to Text Classification in R with quanteda course may be useful for those who wish to specialize in developing software applications that involve text data.
For more career information including salaries, visit:
OpenCourser.com/course/js7sdz/introduction
Reading list
We've selected seven 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 Text Classification in R with quanteda.
Save
Provides a practical introduction to text mining using the R programming language. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning. It would be a valuable resource for students who want to learn how to use text mining techniques to extract insights from data.
Provides a comprehensive overview of the history, theory, and practice of text analysis, including text classification. It would be a valuable resource for students who want to learn more about the broader context of text analysis.
Provides a comprehensive overview of text mining, including text classification. It would be a valuable resource for students who want to learn more about the broader context of text mining and how it is used in practice.
Provides a comprehensive overview of the statistical foundations of natural language processing, including text classification. It would be a valuable resource for students who want to learn more about the theoretical foundations of text classification.
Provides a comprehensive overview of information retrieval, including text classification. It would be a valuable resource for students who want to learn more about the broader context of text classification and how it is used in practice.
Provides a comprehensive overview of speech and language processing, including text classification. It would be a valuable resource for students who want to learn more about the broader context of text classification and how it is used in practice.
Provides a comprehensive overview of computational linguistics, including text classification. It would be a valuable resource for students who want to learn more about the broader context of text classification and how it is used in practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/js7sdz/introduction
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Nicole Baerg
Language
English
Good to know
Develops knowledge of importing textual data into R, turning texts into a machine-readable format, and classifying presidential concession speeches by political party
Builds a foundation for understanding quantitative text analysis in R
Teaches how to assess the accuracy of predictions made through the Naive Bayes algorithm
Assumes no knowledge of textual analysis, making it accessible to beginners
Requires a basic understanding of the R programming language
Focuses on exploring textual data specifically from US Presidential Concession Speeches
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