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Intro to Sentiment Analysis: What it is, how to conduct it, and what are its limitations?

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

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

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Intro to Sentiment Analysis: What it is, how to conduct it, and what are its limitations?

  • 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
  • 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
  • 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