1. Open access and data processing of Social Media (Twitter) data – a new and valuable consumer research instrument Thierry Worch, Anne Hasted & Hal MacFie

2. Using Twitter for Research – Macro vs Micro The R based macro TwitteR A food product application Possible use in Sensory and Consumer Science © Qi Statistics LtdSlide 2 Overview

3. Online social network and microblog. Open text-based messages of up to 140 characters also known as “Tweets”. Tweets are open: – personal information (what people are doing/feeling); – discussions; – sharing information... Tweets are grouped together according to their content (use of “#word”). People can “follow” friends, celebrities or brands to stay updated. Over 500 million registered users in 2012, generating over 340 millions tweets/day, and handling over 1.6 billion search queries/day. © Qi Statistics LtdSlide 3 What is Twitter?

4. Study from Golder et al. Science 30 September 2011: 1878-1881. Previous studies small samples of American students. Students are exposed to varying academic schedules that constrain when and how much they sleep. Retrospective self-reports, vulnerable to memory error and experimenter demand effects. Researchers have acknowledged the limitations of this methodology but have had no practical means for in situ real- time hourly observation of individual behavior in large and culturally diverse populations over many weeks. © Qi Statistics LtdSlide 4 Diurnal and Seasonal Mood Vary with Work, Sleep, and Day length Across Diverse Cultures MACRO APPLICATION 1

5. Methodology Twitter data access 2.4 million individuals worldwide 509 million messages February 2008 and January 2010 Linguistic Inquiry and Word Count (LIWC) Analysis Negative Term FrequenciesPositive Term Frequencies Time of day © Qi Statistics LtdSlide 6

6. Results Individuals awaken in a good mood that deteriorates as the day progresses—which is consistent with the effects of sleep and circadian rhythm. Seasonal change in baseline positive affect varies with change in day length. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends. © Qi Statistics LtdSlide 7

7. Landsdall-Welfare, Lampos, & Cristianini (University of Bristol, UK). 484 million tweets 9.8 million UK users July 09 to Jan 12 © Qi Statistics LtdSlide 7 Effects of the Recession on Public Mood in the UK MACRO APPLICATION 2

8. © Qi Statistics LtdSlide 8 Results – 4 emotion categories

9. “R by example: mining Twitter for consumer attitudes towards airlines”, by Jeffrey Breen (June 2011) © Qi Statistics LtdSlide 9 Micro Application 1: Airline companies

10. Retrieved from www.theacsi.orgwww.theacsi.org Airlines do not score very high compared to other sectors. © Qi Statistics LtdSlide 10 Airline satisfaction scores

11. © Qi Statistics LtdSlide 11 Example of Tweets How can we access and summarize this data?

12. © Qi Statistics LtdSlide 12 Searching tweets with twitteR

13. © Qi Statistics LtdSlide 13 Game Plan for the Sentiment Analysis

14. © Qi Statistics LtdSlide 14 Sentiment distributions SouthwestUnited Airlines Negative Positive Southwest has much less negative tweets than United Airlines

15. 5 chocolate products/brands: – Cadbury – Twix – Snickers – Hershey – KitKat Once a week for 8 weeks. 7000 tweets per brand. Circle around Manchester with a radius of 500 Miles. English only Duplicated tweets (and re-tweets) removed. © Qi Statistics LtdSlide 15 Micro Application 2: Chocolate Study

16. © Qi Statistics LtdSlide 16 Sentiment Analysis CadburysKitkat Negative Positive

17. © Qi Statistics LtdSlide 17 Classification of the terms tweeted after clean up using the R text mining routine TM 9 sensory descriptors in the top 25 of each product 5 sensory descriptors specific to 2 or less products

18. © Qi Statistics LtdSlide 18 Results (chocolate occasion) Category Terms – 9 descriptors in the top 15 of each product Unique Terms – 2 descriptors specific to 2 or less products

19. Cadbury have been running a competition and this is reflected in high frequency responses. Can see descriptors that appear to define the category Can observe product specific descriptors for sensory and occasion © Qi Statistics LtdSlide 19 Results (chocolate)

20. Usage – TwitteR package " easy " to use ( once you know how) – Large number of texts required – even for micro studies – Linguistic/Text processing software essential Micro Applications - Sensory research – Vocabulary development to define a category – Brand specific attributes – Change in sentiment over time and place Research – Macro – find a strong hypothesis and the numbers will do the rest © Qi Statistics LtdSlide 20 Comments

21. Useful open access research source Methodological research needed Specialised sensory algorithms needed © Qi Statistics LtdSlide 21 Conclusion