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Open access and data processing of Social Media (Twitter) data – a new and valuable consumer research instrument. Thierry Worch, Anne Hasted & Hal MacFie. Overview. Using Twitter for Research – Macro vs Micro The R based macro TwitteR A food product application

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Thierry Worch, Anne Hasted & Hal MacFie

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Thierry worch anne hasted hal macfie

Open access and data processing of Social Media (Twitter) data – a new and valuable consumer research instrument

Thierry Worch, Anne Hasted &Hal MacFie



  • Using Twitter for Research – Macro vs Micro

  • The R based macro TwitteR

  • A food product application

  • Possible use in Sensory and Consumer Science

Slide 2

What is twitter

Whatis Twitter?

  • 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.

Slide 3

Diurnal and seasonal mood vary with work sleep and day length across diverse cultures

Diurnal and Seasonal Mood Vary with Work, Sleep, and Day length Across Diverse Cultures


  • 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.

Slide 4



2.4 million individuals worldwide

509 million messages February 2008 and January 2010

Twitter data access

Linguistic Inquiry and Word Count (LIWC) Analysis

Negative Term Frequencies

Positive Term Frequencies

Time of day

Time of day

Slide 6



  • 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.

Slide 7

Effects of the recession on public mood in the uk

Effects of the Recession on Public Mood in the UK


  • Landsdall-Welfare, Lampos, & Cristianini (University of Bristol, UK).

  • 484 million tweets 9.8 million UK users July 09 to Jan 12

Slide 7

Results 4 emotion categories

Results – 4 emotion categories

Slide 8

Micro application 1 airline companies

Micro Application 1: Airline companies

  • “R by example: mining Twitter for consumer attitudes towards airlines”, by Jeffrey Breen (June 2011)

Slide 9

Airline satisfaction scores

Airline satisfaction scores

  • Retrieved from

  • Airlines do not score very high compared to other sectors.

Slide 10

Example of tweets

Example of Tweets

How can we access and summarize this data?

Slide 11

Searching tweets with twitter

Searching tweets with twitteR

Slide 12

Game plan for the sentiment analysis

Game Plan for the Sentiment Analysis

Slide 13

Sentiment distributions

Sentiment distributions




United Airlines

Southwest has much less negative tweets than United Airlines

Slide 14

Micro application 2 chocolate study

Micro Application 2: ChocolateStudy

  • 5 chocolateproducts/brands:

    • Cadbury

    • Twix

    • Snickers

    • Hershey

    • KitKat

  • Once a week for 8 weeks.

  • 7000 tweets per brand.

  • Circlearound Manchester with a radius of 500 Miles.

  • English only

  • Duplicated tweets (and re-tweets) removed.

Slide 15

Sentiment analysis

Sentiment Analysis





Slide 16

Classification of the terms tweeted after clean up using the r text mining routine tm

Classification of the termstweetedafter clean up using the R textmining routine TM

9 sensorydescriptors in the top 25 of eachproduct

5 sensorydescriptorsspecific to 2 or lessproducts

Slide 17

Results chocolate occasion

Results (chocolate occasion)

CategoryTerms – 9 descriptors in the top 15 of eachproduct

Unique Terms – 2 descriptorsspecific to 2 or lessproducts

Slide 18

Results chocolate

Results (chocolate)

  • 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

Slide 19



  • Usage

    • TwitteRpackage "  easy "   to use ( once you know how)

    • Large numberof textsrequired – even for micro studies

    • Linguistic/Textprocessing software essential

  • Micro Applications - Sensory research

    • Vocabularydevelopment to define a category

    • Brand specificattributes

    • Change in sentiment over time and place

  • Research – Macro

    • find a stronghypothesisand the numberswill do the rest

Slide 20



  • Useful open accessresearch source

  • Methodologicalresearchneeded

  • Specialised sensory algorithmsneeded

Slide 21

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