Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

An 8-Hour Livestream Seminar Taught by Marcus Mann, Ph.D.

 In just 8 hours (over 2 days), you will learn various approaches to analyzing sentiment in text, when to use each, and how to report your findings to your audience. We hope you enjoy the course.

Sentiment analysis has long been an invaluable tool for business and academic researchers interested in identifying and measuring positive and negative emotion in text. Whether trying to analyze online reviews of a new product or how political partisans talk about their opponents on social media, these methods have allowed the quantification and clear communication of these qualities in large text corpora. More recently, ‘sentiment analysis’ has evolved to incorporate any method that uses pre-existing ‘dictionaries’ of words to deductively extract meaning from texts – especially text collected from online digital trace data – using assigned scores relating to a variety of emotional and cognitive states.

This course is designed as an introduction to sentiment analysis for those with little or no experience in this area. Topics will include differentiating sentiment analysis from other text analysis approaches; basic methods of extracting, cleaning, and preparing text for analysis; introductions to popular text analysis dictionaries; and incorporating sentiment analysis results into regression and other frameworks.

This course will introduce you to ‘sentiment analysis’ by first offering several examples of how these methods have been used in research and business contexts and how they differ from other text analysis methods. After orienting you with these methods and what they are (and are not) capable of, you will be guided through several hands-on examples of how to prepare text data for analysis, how to run text data through sentiment dictionaries, and then how to incorporate results into regression or descriptive frameworks where results are contextualized for a specific audience.

You will also be introduced to a range of sentiment analysis dictionaries and taught how to search for and discover dictionaries for yourself so that you can use these tools as new research questions or new data prompt the need for new and different analyses.

At the end of this course, you will know:

• How sentiment analysis is different from other text analysis methods and when to use it.
• How to apply different sentiment analysis dictionaries to your data.
• How to prepare your text for analysis.
• How to incorporate results from sentiment analyses into broader analytical frameworks.
• How to independently explore and use different dictionaries according to your emerging questions and needs.