Sumanta Basu is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. Broadly, his research interests are structure learning and the prediction of large systems from data, with a particular emphasis on developing learning algorithms for time series data. Professor Basu also collaborates with biological and social scientists on a wide range of problems, including genomics, large-scale metabolomics, and systemic risk monitoring in financial markets. His research is supported by multiple awards from the National Science Foundation and the National Institutes of Health. At Cornell, Professor Basu teaches “Introductory Statistics” for graduate students outside the Statistics Department and “Computational Statistics” for Statistics Ph.D. students. He also serves as a faculty consultant at Cornell Statistical Consulting Unit, which assists the broader Cornell community with various aspects of analyzing empirical research. Professor Basu received his Ph.D. from the University of Michigan and was a postdoctoral scholar at the University of California, Berkeley, and Lawrence Berkeley National Laboratory. Before he received his Ph.D, Professor Basu was a business analyst, working with large retail companies on the design and data analysis of their promotional campaigns.
Transforming Text to Numeric SentimentsCornell Course
Course Overview
In today's data-driven world, being able to quantify and analyze sentiment in text is a powerful skill for understanding customer feedback, social media trends, and more. This course gives you the expertise to transform text into meaningful sentiment scores using key libraries like AFINN, Bing, and NRC.
You will begin by working with these sentiment analysis tools to categorize and quantify emotional tones in documents. From there, you will calculate and visualize sentiment scores using tools like line plots, bar charts, and word clouds. Finally, you will compare sentiment across multiple documents and track changes over time.
By the end of the course, you will be ready to interpret and act on sentiment trends in real-world applications, offering valuable insights for business strategies, customer relations, and market analysis.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Mastering NLP Fundamentals
- Exploring Summarization and Visualization
Key Course Takeaways
- Describe and compare common sentiment libraries for converting text to numeric or categorical data
- Summarize and visualize sentiments for answering univariate questions
- Summarize and visualize sentiments for answering multivariate questions and examining the evolution of sentiments over time
How It Works
Course Authors
Sreyoshi Das designs and offers courses on the applications of statistics and data science in the industry, with specific emphasis in the areas of economics and finance. Her courses aim to integrate academic training with hands-on work experience.
Before joining Cornell in 2022, Professor Das worked in economic consulting, where she developed a variety of quantitative and qualitative analyses to support testifying experts, client attorneys, government agencies, and corporations. In 2017, Professor Das received her Ph.D. in Economics from the University of Michigan, where she conducted research on banking and systemic risk, financial markets in emerging economies, and behavioral macroeconomics.
Who Should Enroll
- Data scientists
- Computer scientists
- Analysts
- User behavior and UX teams
- Researchers
- Social scientists
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