Class lectures for Comm 399: Fundamentals of Social Media, Fall 2012, Department of Communication, Shepherd University.

1. What are they saying?
Sentiment Analysis
Professor Matthew Kushin, PhD
Shepherd University | Department of Mass Communication | 2012

2. Preview
 Last class:
 Looked at what people say about a brand online
 Today, we’ll explore:
 Can we more systematically evaluate text content (such as
tweets)?

3. Defined
 Sentiment analysis – process of categorizing text, based on
the “sentiment” or “feelings” embedded in the message.
 Aka “opinion mining”
 For assessing opinions
 Form of content analysis – systematic process of coding
content of media for interpretation

4. Simple Example
 Tweet:
 “Can’t wait to see Zan the Ram at the game this weekend!!!”
 Sentiment:
 Positive
 Problem
 How do we know that this text is positive?

5. Basics: How it works
 A database of words and symbols (e.g., !) is created.
 Each word is assigned a value
 Positive = 1
 Negative = -1
 Neutral = 0
 Example: “love” = 1; “hate” =2; “blue” =0

6. How it works, contd
 Computers or person evaluates each piece of data (e.g., a
Tweet), searching for words in database.
 Total number of positive/negative/neutral counted in data,
and a sentiment score or % is given

7. Example
 Tweets about Shepherdstown over a one-week period:
 60% positive
 20% negative
 20% neutral

8. Usefulness

9. Potential, potential
 Attitude towards your brand
 Perception of products, ideas, brands, people, etc.
 Reputation management
 Able to respond to posts
 Evaluate over time to see if sentiment is changing as part of
campaign goals

10. Example: Taco Bell Beef!
 Last class
 an unrepresentative sample of Tweets we happen to look at
 Sentiment offers:
 Much more systematic evaluation of tweets
 Evaluate thousands of social media posts
 Very quick & little cost

11. Tools

12. Tools
 Many tools (paid and free) exist for assessing sentiment
 Study of linguistics is applied backed by years of research &
understanding of human language.
 Common limitations:
 Language contains context
 Is subjective
 Inability to assess sarcasm & other nuances of human
communication

13. Example
 http://twitrratr.com/

14. Key Considerations
 We must interpret what the sentiment means
 Sentiment alone is only a minor indicator of the true feelings
of the crowd.
 Need to ask “Why is it positive/negative?”
 “What products do people mention?”

15. Beyond Basic Sentiment
 Computer-assisted content analysis of social media posts
virtually limitless potential
 More detail than “pos” “neg” “neutral”
 What about:
 “satisfied” “dissatisfied” “concerned” “informed” “afraid” “glad”
etc.
 Count all mentions of ANY term
 Ex: product mentions in tweets: “Fries” “McRib” “McFlurry”

16. Participation: Obama v. Romney
Sentiment
 Getting the PDF files:
 Obama Tweets PDF: http://bit.ly/402_ObamaTweets
 Romney Tweets PDF: http://bit.ly/402_RomneyTweets