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![Semantic differential
• Semantic differential consists in three value:
– Evaluation (Good – Bad)
– Activity (Active – Passive)
– Potency (Strong – Weak)
• For each word from the 1,000 most frequently
used words these three values are measured.
• Each value belongs to interval [-4.6, 4.6]](https://image.slidesharecdn.com/measurementpresentation28page29-140929091009-phpapp02/85/Measurement-presentation-28-page-29-8-320.jpg)
Measurement presentation %28 page%29
- 1. Measuring customer caretalk in Twitter Dr Ruth Page, Professor Jeremy Levesley University of Leicester rep22@le.ac.uk, @ruthtweetpage jl1@le.ac.uk www.le.ac.uk
- 2. Overview • Linguisticmethods for quantitative analysis – Semantic Differential – Corpus linguistics – Discourse Analysis • Why might this be useful? – Identifying distinctive patterns in communication – Customer care training • How metrics can be turned into indices
- 3. Data sets Pagedata • Data – 177,735 tweets • 100 publically available accounts – 40 companies – 30 celebrities – 30 ‘ordinary’ accounts • Gathered in 2010 and 2012 – Hashtags (Page 2012) – Apologies (Page 2014) Precise data • BT Care – 4014 tweets – 69,976 words • HSBC UK Help – 3882 tweets – 78,375 words
- 4. Methods: Scraping •Data capture (Page) – Bespoke python code that worked with the Twitter API to scrape all public posts from named accounts – Automatically sorted tweets • Updates • Addressed messages (starting with @username) • Retweets • Converted files to plain text
- 5. Methods: Sampling •travel: – @bluejet, @luxorlv, @southwestair, @british_airways, @londonmidland, @connectbyhertz, @carnivalcruise • entertainment: – @directv, @marvel, @travelchannel, @tvguide • food: – @sainsburys, @waitrose, @tastidlite, @popeyeschicken, @starbucks, @dunkindonuts, @wholefoods, @uktesco, @dunkindonuts • technology: – @emccorp, @itunesmusic, @dellcares, @costcomcares • finance: – @hoover, @hrblock, @zappos, @wachovia, @intuit • sport: – @chargers, @chicagobulls • retail: – @selfridges, @americanapparel, @karenmillen, @reiss, @marksandspencer, @rubbermaid, @johnlewisretail.
- 6. Frequency distribution Cumulative frequency Frequency End-to-begin cumulative frequency
- 7.
- 8. Semantic differential •Semantic differential consists in three value: – Evaluation (Good – Bad) – Activity (Active – Passive) – Potency (Strong – Weak) • For each word from the 1,000 most frequently used words these three values are measured. • Each value belongs to interval [-4.6, 4.6]
- 9. Visualization • Rightfigure depicts the distribution of tweets in the space of the first three principal components calculated for the first 150 words • We can see dense cone and small cluster outside the cone
- 10. Visualization • Wecalculate two subsets of the first 150 words which realize an 80% covering of tweets. • We calculate three subsets of the first 150 words which realize a 70% covering of tweets.
- 11. Visualization • Rightfigures depict the distribution of tweets in the space of the first three principal components calculated for the first 80% covering • We can see three clusters
- 12. layered structure •Right figures depict the distribution of tweets in the space of the first three principal components calculated for the first 70% covering • We can see Layered structure
- 13. Visualization • Rightfigures depict the distribution of tweets in the space of the first three principal components calculated for the second 70% covering • We can see layered structure
- 14. Question 1 •What kinds of messages do different groups of Twitter members post to their accounts? • Methods – Quantifying the number of each type of post
- 15. INSIGHT: Distribution oftweet types (2010) All groups favoured updates, with celebrities most of all Twitter is an environment for ‘broadcasting’ one-to-many messages ‘Conversational’ one-to-one messages were less
- 16. INSIGHT: Distribution oftweet types (2012) Corporate tweeting behaviour changed and becomes more ‘conversational’ What’s distinctive about the corporate addressed messages?
- 17. Using Corpus-based methods • Corpus – a definition – Collection of representative texts – Machine readable form • Concordancing tools – Antconc (Laurence 2014) - Freeware – Wordsmith Tools, Wmatrix – Proprietary • Search and sort lexical strings • Compare with other corpora
- 18. Corpus linguistics: Basicsteps Start by examining…. 1.Frequency of words 2.Keywords in Context (KWIC) 3.Collocations Clusters of words that repeatedly occur together
- 19. Is the frequencypattern specific to this dataset? Keyness list • ‘Keyness’ – Statistical over-use of words – I compared the corporate addressed messages with all tweets in my dataset • INSIGHT: – The items in the keyness list cluster together and are typically found in apologies
- 20. HSBC, BT Careand ‘Page’ data • INSIGHT • HSBC UK Help and BT Care apologise even more than the companies in my dataset!
- 21. Why are apologiesso important? • Twitter is a public environment where customers can complain • Damage to the company’s reputation • Apologies need to rebuild reputation and re-establish rapport between company and customer
- 22. Example: KWIC listfor HSBC’s sorry
- 23. Methods: Discourse Analysis • Manually extracted all examples of apologies from data (c.1200 egs) • Coded manually in Excel • Features identified by other researchers interested in apologies • Other communicative features • Formulae which indicate the apology • Problem restated in the apology • Explanation or account • Offer of repair • Greeting • Naming • Additional questions or instructions • Emoticons and conversational features (discourse markers)
- 24. Do companies repeatthe problem or not? Companies tend to avoid repeating the problem in their apology. This enables them to preserve their reputation, but it can appear impersonal. BT Care typically uses ‘vague language’ to avoid restating the problem HSBC UK Help typically restates the problem
- 25. Do companies explain why the problem occurred? Companies do not often explain why a problem occurred. But when they do, it typically downplays their role in the offence that prompted the complaint. The effect can be to mitigate damage to reputation
- 26. Do companies makean ‘offer of repair’? Offering recompense to the customer can be a way to rebuild reputation and re-establish rapport with the customer. It’s not always appropriate though, and depends on the company in question.
- 27. Personalising the message • Signatures – 37% of apologies by companies – 0 of apologies by ordinary accounts – 100% by HSBC Help UK – 0 by BT Care • Name of customer – 19% of apologies by companies – 11% of apologies by ordinary accounts – 69% by HSCB Help UK – 0 by BT Care
- 28. ‘Rapport’ talk: Greetings and Emoticons HSBC BT Care Hi 2399 2815 Hello 230 14 Good afternoon 344 0 Good evening 306 0 Good morning 357 14 3636 2844 INSIGHT The style of an apology can be more or less formal. More conversational features like greetings and signals of emotional response like emoticons can be used to project rapport with the customer. In my data, 19% of companies and none of the ordinary accounts used greetings. Six percent of the companies and 25% of the ordinary accounts used emoticons. HSBC uses ‘rapport’ features more often than BT Care (figures given per million words) HSBC BT Care :) 2118 343 :( 77 29 ;-) 89 0 :-) 115 57 2399 429
- 29. Does the customerneed to respond again? Companies may not be able to respond completely to the complaint in Twitter. They may need more information or ask a third party to respond. This is risky as it means that the communication chain can break down leading to greater customer dissatisfaction.
- 30. Comparison of questionson the HSBC Help and BT Care accounts HSBC UK Help • 31% of all company tweets contained a punctuated question • 15% of the questions checked if the customer had been in touch • 10% asked if further help was needed BT Care • 44% of all company tweets contained a punctuated question • No questions asked if the customer had been in touch or needed further help
- 31. Summary of BTCare and HSBC HSBC UK Help • Risk reputation by restating the problem • Build rapport by – Personalising their tweets by always signing off and frequently use the customer’s name – Use greetings and emoticons • Use follow up questions to check customers’ needs BT Care • Protect reputation by rarely restating the problem and use explanations to defer blame • Limited rapport – Rarely if ever sign off and never use the customer’s names – Rarely use conversational features • Never use follow up questions to close the apology
- 32. Application for clients • What makes an apology good PR? – Different factors – Not all linguistic (e.g. timeliness) – Different people will value different aspects • Successful customer care is not mechanistic • But analysis can identify areas of need and then training can be developed to improve practice
Editor's Notes
- #16 In 2010, the aggregated distribution of types of tweet show that for all 3 groups, the preferred type of tweet is the update, or the 1-to-many broadcast. However, this preference is more marked for the celebrities and least marked for ordinary twitter users. This difference is perhaps understandable: it is more economic for a celebrity to publish a single update to their sizable fan base than to write individually addressed messages to all of their followers.
- #17 In 2012, this communicative pattern continues for the Celebrity and Ordinary accounts, but not for the corporations. The pattern reverses here and Addressed messages account for a greater proportion of the posts that are published. This is quite striking, especially if we compare it with the modified retweets that are found in the updates.