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Ahmad Varasteh
By the end of this project you will learn how to preprocess your text data for sentimental analysis.
So in this project we are going to use a Dataset consisting of data related to the tweets from the 24th of July, 2020 to the 30th of August 2020 with COVID19 hashtags. We are going to use python to apply sentimental analysis on the tweets to see people's reactions to the pandemic during the mentioned period. We are going to label the tweets as Positive, Negative, and neutral. After that, we are going to visualize the result to see the people's reactions on Twitter.
Read more
By the end of this project you will learn how to preprocess your text data for sentimental analysis.
So in this project we are going to use a Dataset consisting of data related to the tweets from the 24th of July, 2020 to the 30th of August 2020 with COVID19 hashtags. We are going to use python to apply sentimental analysis on the tweets to see people's reactions to the pandemic during the mentioned period. We are going to label the tweets as Positive, Negative, and neutral. After that, we are going to visualize the result to see the people's reactions on Twitter.
Read more
By the end of this project you will learn how to preprocess your text data for sentimental analysis.
So in this project we are going to use a Dataset consisting of data related to the tweets from the 24th of July, 2020 to the 30th of August 2020 with COVID19 hashtags. We are going to use python to apply sentimental analysis on the tweets to see people's reactions to the pandemic during the mentioned period. We are going to label the tweets as Positive, Negative, and neutral. After that, we are going to visualize the result to see the people's reactions on Twitter.
Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/7wo7qz/sentimental
What's inside
Syllabus
Sentimental Analysis on COVID-19 Tweets using python
By the end of this project, you will learn how to preprocess your text data for sentimental analysis. So in this project, we are going to use a Dataset consisting of data related to the tweets from 24th of July 2020 to the 30th of August 2020 with covid19 hashtags. we are going to use python to apply sentimental analysis on the tweets to see people's reactions to the pandemic during the mentioned period. We are going to label the tweets as Positive, Negative, and neutral. After that we are going to visualize the result to see the people's reactions on Twitter.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides students with a hands-on approach to practical application
Taught by Ahmad Varasteh who has notable experience in course subject
Provides beginners with introductory knowledge and skills
Introduces learners to industry standard methods and practices
Incorporates real-world examples and case studies
Students will enhance their understanding of text processing and data analysis
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Reviews summary
Data science: analyzing covid-19 tweets
Learners say this course is a well-received introduction to data science. It teaches students how to analyze Twitter data for a variety of purposes, including sentiment analysis and data visualization. The course is well-structured and the material is engaging. The instructor is knowledgeable and helpful. Overall, this is a great course for anyone interested in learning more about data science.
Good Starting Point for Beginners
"Good starting project for data visualizing field."
Knowledgeable and Helpful Instructor
"The instructor is knowledgeable and helpful."
Well-Structured Lessons
"The course is well-structured and the material is engaging."
Engaging and Informative Content
"It teaches students how to analyze Twitter data for a variety of purposes, including sentiment analysis and data visualization."
Insufficient Explanation of Theory and Code
"I would appreciate if he could give more explanation on the theory (polarity score) and code concept (''join)."
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 Sentimental Analysis on COVID-19 Tweets using python with these
activities:
Read article on Twitter Sentiment Analysis
Show steps
Refresh your understanding of sentiment analysis to prepare for this course.
Browse courses on
Sentiment Analysis
Show steps
-
Find an article on Twitter sentiment analysis.
-
Read the article and take notes on the key concepts.
Follow a tutorial on how to use a specific sentiment analysis library in Python.
Show steps
Expand your knowledge by practicing with real-world examples.
Show steps
-
Find a tutorial on how to use a sentiment analysis library in Python.
-
Follow the tutorial.
Join a study group or online community for sentiment analysis.
Show steps
Connect with other learners and discuss course material.
Show steps
-
Find a study group or online community for sentiment analysis.
-
Join the group.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Practice sentiment analysis on sample tweets
Show steps
Practice sentiment analysis to reinforce the concepts covered in this course.
Browse courses on
Sentiment Analysis
Show steps
-
Find a dataset of tweets with sentiment labels.
-
Write a Python script to perform sentiment analysis on the tweets.
-
Evaluate the performance of your script.
Discuss Twitter sentiment analysis with peers
Show steps
Discuss sentiment analysis and share insights with your peers.
Browse courses on
Sentiment Analysis
Show steps
-
Find a peer or group of peers to discuss with.
-
Prepare talking points or questions to discuss.
-
Meet with your peers and discuss Twitter sentiment analysis.
Practice solving coding problems related to sentimental analysis in python
Show steps
Develop your coding skills and strengthen your understanding of sentiment analysis.
Show steps
-
Find practice problems online or in a textbook.
-
Solve the problems.
-
Review your solutions.
Participate in a workshop on sentiment analysis.
Show steps
Gain in-depth knowledge and practical experience in sentiment analysis.
Show steps
-
Find a workshop on sentiment analysis.
-
Attend the workshop.
Write a short blog post summarizing the key concepts of sentiment analysis.
Show steps
Improve your understanding by explaining concepts to others and solidify your knowledge on core topics.
Show steps
-
Identify the key concepts of sentiment analysis.
-
Write a blog post explaining these concepts.
-
Publish your blog post.
Attend a conference or meetup on sentiment analysis.
Show steps
Connect with other professionals in the field and learn about the latest trends.
Show steps
-
Find a conference or meetup on sentiment analysis.
-
Attend the event.
Volunteer for a project related to sentiment analysis.
Show steps
Gain practical experience and contribute to the community.
Show steps
-
Find a project related to sentiment analysis.
-
Apply for the project.
Follow tutorials on advanced sentiment analysis techniques
Show steps
Learn advanced sentiment analysis techniques to enhance your understanding.
Browse courses on
Sentiment Analysis
Show steps
-
Find tutorials on advanced sentiment analysis techniques.
-
Follow the tutorials and take notes on the key concepts.
-
Implement the techniques in your own sentiment analysis scripts.
Build a Twitter sentiment analysis dashboard
Show steps
Create a dashboard to visualize the sentiment of tweets related to COVID-19.
Browse courses on
Sentiment Analysis
Show steps
-
Collect a dataset of tweets related to COVID-19.
-
Perform sentiment analysis on the tweets.
-
Create a dashboard to visualize the sentiment of the tweets.
Develop a dataset of tweets and perform sentimental analysis on them.
Show steps
Apply your skills and gain hands-on experience working with actual data.
Show steps
-
Collect a dataset of tweets.
-
Preprocess the data.
-
Perform sentimental analysis on the data.
-
Visualize the results.
Read article on Twitter Sentiment Analysis
Show steps
Refresh your understanding of sentiment analysis to prepare for this course.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Find an article on Twitter sentiment analysis.
- Read the article and take notes on the key concepts.
Follow a tutorial on how to use a specific sentiment analysis library in Python.
Show steps
Expand your knowledge by practicing with real-world examples.
Show steps
Show steps
Show steps
- Find a tutorial on how to use a sentiment analysis library in Python.
- Follow the tutorial.
Join a study group or online community for sentiment analysis.
Show steps
Connect with other learners and discuss course material.
Show steps
Show steps
Show steps
- Find a study group or online community for sentiment analysis.
- Join the group.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Practice sentiment analysis on sample tweets
Show steps
Practice sentiment analysis to reinforce the concepts covered in this course.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Find a dataset of tweets with sentiment labels.
- Write a Python script to perform sentiment analysis on the tweets.
- Evaluate the performance of your script.
Discuss Twitter sentiment analysis with peers
Show steps
Discuss sentiment analysis and share insights with your peers.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Find a peer or group of peers to discuss with.
- Prepare talking points or questions to discuss.
- Meet with your peers and discuss Twitter sentiment analysis.
Practice solving coding problems related to sentimental analysis in python
Show steps
Develop your coding skills and strengthen your understanding of sentiment analysis.
Show steps
Show steps
Show steps
- Find practice problems online or in a textbook.
- Solve the problems.
- Review your solutions.
Participate in a workshop on sentiment analysis.
Show steps
Gain in-depth knowledge and practical experience in sentiment analysis.
Show steps
Show steps
Show steps
- Find a workshop on sentiment analysis.
- Attend the workshop.
Write a short blog post summarizing the key concepts of sentiment analysis.
Show steps
Improve your understanding by explaining concepts to others and solidify your knowledge on core topics.
Show steps
Show steps
Show steps
- Identify the key concepts of sentiment analysis.
- Write a blog post explaining these concepts.
- Publish your blog post.
Attend a conference or meetup on sentiment analysis.
Show steps
Connect with other professionals in the field and learn about the latest trends.
Show steps
Show steps
Show steps
- Find a conference or meetup on sentiment analysis.
- Attend the event.
Volunteer for a project related to sentiment analysis.
Show steps
Gain practical experience and contribute to the community.
Show steps
Show steps
Show steps
- Find a project related to sentiment analysis.
- Apply for the project.
Follow tutorials on advanced sentiment analysis techniques
Show steps
Learn advanced sentiment analysis techniques to enhance your understanding.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Find tutorials on advanced sentiment analysis techniques.
- Follow the tutorials and take notes on the key concepts.
- Implement the techniques in your own sentiment analysis scripts.
Build a Twitter sentiment analysis dashboard
Show steps
Create a dashboard to visualize the sentiment of tweets related to COVID-19.
Browse courses on
Sentiment Analysis
Show steps
Show steps
Show steps
- Collect a dataset of tweets related to COVID-19.
- Perform sentiment analysis on the tweets.
- Create a dashboard to visualize the sentiment of the tweets.
Develop a dataset of tweets and perform sentimental analysis on them.
Show steps
Apply your skills and gain hands-on experience working with actual data.
Show steps
Show steps
Show steps
- Collect a dataset of tweets.
- Preprocess the data.
- Perform sentimental analysis on the data.
- Visualize the results.
Career center
Learners who complete Sentimental Analysis on COVID-19 Tweets using python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and interprets data to help businesses make informed decisions. This course on "Sentimental Analysis on COVID-19 Tweets using Python" can be useful for aspiring Data Analysts as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which are essential skills for Data Analysts.
Data Engineer
Data Engineer
A Data Engineer builds and maintains the infrastructure that stores and processes data. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Data Engineers as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which are essential skills for Data Engineers.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems and develop new products and services. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Data Scientists as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which are essential skills for Data Scientists.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and data to help businesses make informed decisions. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Business Analysts as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Business Analysts better understand business trends and make informed recommendations.
User Experience Researcher
User Experience Researcher
A User Experience Researcher studies how users interact with products and services in order to improve the user experience. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring User Experience Researchers as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help User Experience Researchers better understand user needs and improve the design of products and services.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve business problems. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Machine Learning Engineers as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which are essential skills for Machine Learning Engineers.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations and advise clients on financial matters. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Financial Analysts as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Financial Analysts better understand market trends and make informed investment recommendations.
Product Manager
Product Manager
A Product Manager develops and manages products from inception to launch. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Product Managers as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Product Managers better understand user needs and develop products that meet those needs.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Quantitative Analysts as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Quantitative Analysts better understand market trends and make informed investment decisions.
Market Research Analyst
Market Research Analyst
A Market Research Analyst conducts research to understand market trends and consumer behavior. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Market Research Analysts as it provides a foundation in sentiment analysis techniques and the use of Python for data analysis. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Market Research Analysts better understand consumer sentiment and make informed recommendations.
Social Media Manager
Social Media Manager
A Social Media Manager develops and implements social media strategies to engage with customers and promote a brand. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Social Media Managers as it provides a foundation in sentiment analysis techniques and the use of Python for data analysis. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Social Media Managers better understand audience sentiment and tailor their strategies accordingly.
Marketing Manager
Marketing Manager
A Marketing Manager develops and implements marketing campaigns to promote a brand and its products or services. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Marketing Managers as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Marketing Managers better understand consumer sentiment and tailor their marketing campaigns accordingly.
Consultant
Consultant
A Consultant provides advice and guidance to businesses on a variety of topics, including strategy, operations, and technology. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Consultants as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Consultants better understand business trends and make informed recommendations to their clients.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Software Engineers as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Software Engineers better understand user behavior and improve the design and functionality of software applications.
Web Developer
Web Developer
A Web Developer designs and develops websites and web applications. This course on "Sentimental Analysis on COVID-19 Tweets using Python" may be useful for aspiring Web Developers as it provides a foundation in data analysis techniques and the use of Python for data manipulation and visualization. The course covers topics such as data preprocessing, sentiment analysis, and visualization, which can help Web Developers better understand user behavior and improve the design and functionality of websites and web applications.
For more career information including salaries, visit:
OpenCourser.com/course/7wo7qz/sentimental
Reading list
We've selected 11 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
Sentimental Analysis on COVID-19 Tweets using python.
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Provides a comprehensive introduction to deep learning, covering topics such as neural networks, convolutional neural networks, recurrent neural networks, and more. It classic textbook that is widely used in academic institutions.
Provides a comprehensive introduction to artificial intelligence, covering topics such as machine learning, natural language processing, computer vision, and more. It classic textbook that is widely used in academic institutions.
Covers the basics of sentiment analysis, including different techniques for sentiment analysis, such as supervised learning, unsupervised learning, and lexicon-based approaches. It also covers more advanced topics such as aspect-based sentiment analysis and sentiment analysis in social media.
Provides a comprehensive introduction to pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It classic textbook that is widely used in academic institutions.
Provides a comprehensive introduction to machine learning with Scikit-Learn, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It also covers more advanced topics such as deep learning, natural language processing, and computer vision.
Provides a comprehensive introduction to natural language processing, covering topics such as text preprocessing, tokenization, stemming, lemmatization, stop word removal, and more. It also covers more advanced topics such as sentiment analysis, machine translation, and text classification.
Provides a comprehensive introduction to natural language processing with Python and NLTK, covering topics such as text preprocessing, tokenization, stemming, lemmatization, stop word removal, and more. It also covers more advanced topics such as sentiment analysis, machine translation, and text classification.
Provides a comprehensive introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It also covers more advanced topics such as deep learning, natural language processing, and computer vision.
Provides a comprehensive introduction to data analysis with Python, covering topics such as data cleaning, data exploration, data visualization, and more. It also covers more advanced topics such as machine learning, natural language processing, and time series analysis.
Provides a comprehensive introduction to deep learning with TensorFlow, covering topics such as neural networks, convolutional neural networks, recurrent neural networks, and more. It also covers more advanced topics such as natural language processing, computer vision, and speech recognition.
Provides a comprehensive introduction to deep learning, covering topics such as neural networks, convolutional neural networks, recurrent neural networks, and more. It also covers more advanced topics such as natural language processing, computer vision, and speech recognition.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7wo7qz/sentimental
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ahmad Varasteh
Language
English
Good to know
Provides students with a hands-on approach to practical application
Taught by Ahmad Varasteh who has notable experience in course subject
Provides beginners with introductory knowledge and skills
Introduces learners to industry standard methods and practices
Incorporates real-world examples and case studies
Students will enhance their understanding of text processing and data analysis
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