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


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

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

  1. 1. What are they saying? Sentiment Analysis Professor Matthew Kushin, PhDShepherd University | Department of Mass Communication | 2012
  2. 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. 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. 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. 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. 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. 7. Example Tweets about Shepherdstown over a one-week period:  60% positive  20% negative  20% neutral
  8. 8. Usefulness
  9. 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. 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. 11. Tools
  12. 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. 13. Example
  14. 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. 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. 16. Participation: Obama v. RomneySentiment Getting the PDF files:  Obama Tweets PDF:  Romney Tweets PDF: