1. Time series Word frequencies Sentiment analysis Twitter Analysis for the Social Sciences and Humanities

2. Twitter Data: Quick and Slow 1. Quick: http://topsy.com search, then clickhttp://topsy.com 2. Slow: Download Mozdeh http://mozdeh.wlv.ac.ukhttp://mozdeh.wlv.ac.uk, collect data in advance then analyse Tweets per hour including ‘Nobel’ Tweets per day including ‘Nobel’

3. Gathering Twitter data Tweets can be obtained via Mozdeh Also: Chorus http://www.chorusanalytics.co.uk/ NodeXL and other softwarehttp://www.chorusanalytics.co.uk/ Must be gathered in near to real time Not available after about 2 weeks Gather using keyword or phrase searches If historical tweets needed, buy from a data provider

4. Twitter Time Series Analysis with Mozdeh: Step by Step Download free from http://mozdeh.wlv.ac.uk for Windowshttp://mozdeh.wlv.ac.uk

11. Search engine part of Mozdeh – for the saved tweets

12. Sentiment-based searches

13. Gender-based searches

14. Identifies words that are Relatively frequent for the query

17. Co-word analysis Am automatic method to compare different subsets of tweets for key differences Identifies words that are relatively frequent compared to one keyword (or gender) compared to another

18. The above example will compare the words in tweets containing book and read by gender

19. Words more common in male tweets containing “book” than in female tweets containing “book”

20. Words more common in ? tweets than in ? Tweets (male or female)

21. Words more common in ? tweets than in ? Tweets (male or female) A statistical measure of the significance of the difference between genders

22. Twitter research ideas Monitor keyword searches for a topic Analysis ideas Time series – identify trends & causes of any peaks Content analysis of random sample tweets – why is the topic tweeted? Gender/sentiment/high frequency keywords Network analysis of tweeters/tweeting & qualitative analysis of key tweeters – who is tweeting and who is successful?

23. How and why scholars cite on Twitter – Priem & Costello Example of a Twitter study using interviews and content analysis of tweets Gathered and analysed a sample of tweets sent by scholars Concluded that: “Twitter citations are also uniquely conversational, reflecting a broader discussion crossing traditional disciplinary boundaries.”

24. High frequency word analysis An analysis of high frequency words for Nobel prizes found tweets about: Alternative winners’ names non- academic prizes only Gender references Female winner only (9% mentioned her gender). Expressions of Sentiment literature prize only (42% positive) A quick analysis can give new insights.

25. Sentiment Strength Detection in the Social Web -SentiStrength Detect positive and negative sentiment strength in short informal text Develop workarounds for lack of standard grammar and spelling Harness emotion expression forms unique to MySpace or CMC (e.g., :-) or haaappppyyy!!!) Classify simultaneously as positive 1-5 AND negative 1-5 sentiment Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social Web.Sentiment strength detection for the social Web Journal of the American Society for Information Science and Technology, 63(1), 163-173. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text.Sentiment strength detection in short informal text Journal of the American Society for Information Science and Technology, 61(12), 2544-2558.

26. SentiStrength Algorithm - Core List of 2,489 positive and negative sentiment term stems and strengths (1 to 5), e.g. ache = -2, dislike = -3, hate=-4, excruciating -5 encourage = 2, coolest = 3, lover = 4 Sentiment strength is highest in sentence; or highest sentence if multiple sentences

27. My legs ache. You are the coolest. I hate Paul but encourage him. -2 3 -4 2 1, -2 positive, negative 3, -1 2, -4

28. Extra sentiment methods spelling correction nicce -> nice booster words alter strength very happy negating words flip emotions not nice repeated letters boost sentiment/+ve niiiice emoticon list :) =+2 exclamation marks count as +2 unless –ve hi! repeated punctuation boosts sentiment good!!! negative emotion ignored in questionsu h8 me? Sentiment idiom list shock horror = -2 Online as http://sentistrength.wlv.ac.uk/http://sentistrength.wlv.ac.uk/

29. Tests against human coders Data set Positive scores - correlation with humans Negative scores - correlation with humans YouTube0.5890.521 MySpace0.6470.599 Twitter0.5410.499 Sports forum0.5670.541 Digg.com news0.3520.552 BBC forums0.2960.591 All 6 data sets 0.5560.565 SentiStrength agrees with humans as much as they agree with each other 1 is perfect agreement, 0 is random agreement

30. Why the bad results for BBC? (and Digg) Irony, sarcasm and expressive language e.g., David Cameron must be very happy that I have lost my job. It is really interesting that David Cameron and most of his ministers are millionaires. Your argument is a joke. $

31. Example – sentiment in major media events Analysis of a corpus of 1 month of English Twitter posts (35 Million, from 2.7M accounts) Automatic detection of spikes (events) Assessment of whether sentiment changes during major media events

32. Automatically-identified Twitter spikes 9 Mar 2010 9 Feb 2010 Proportion of tweets mentioning keyword Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Sentiment in Twitter events Journal of the American Society for Information Science and Technology, 62(2), 406-418.

33. Chile matching posts Sentiment strength Subj. Increase in –ve sentiment strength 9 Feb 2010 Date and time 9 Mar 2010 Av. +ve sentiment Just subj. Av. -ve sentiment Just subj. Proportion of tweets mentioning Chile

34. #oscars % matching posts Sentiment strength Subj. Increase in –ve sentiment strength Date and time 9 Feb 2010 9 Mar 2010 Av. +ve sentiment Just subj. Av. -ve sentiment Just subj. Proportion of tweets mentioning the Oscars

35. Sentiment and spikes Statistical analysis of top 30 events: Strong evidence that higher volume hours have stronger negative sentiment than lower volume hours No evidence that higher volume hours have different positive sentiment strength than lower volume hours => Spikes are typified by small increases in negativity

36. Summary Tweets gathered free with Mozdeh Search tweets and conduct content analysis Time series analysis graphs – trends over time Identify topics causing high sentiment Identify differences by gender Identify important topics by high freq. keywords Identify important topic differences by co-words Pilot test your ideas first Gather tweets in advance for a period of time Can give quick insights into your research goals – or can be a primary research method