This document discusses social media analytics and sentiment analysis tools. It notes that while there is a lot of data on social media, analyzing and making sense of it is challenging due to issues like ambiguous language, sarcasm and misspellings. Commonly used tools often don't work well on social media texts. The document then introduces GATE and GATE Cloud as tools that can help address these challenges through approaches like knowledge-based linguistic analysis and pre-processing tailored for social media. GATE Cloud also offers the benefits of scalability, no hardware costs and easy integration of text analytics workflows into applications.

1. Social Media Analytics as a Service
Dr. Diana Maynard
University of Sheffield, UK

2. We are all connected to each other...
● Information,
thoughts and
opinions are
shared
prolifically on
the social web
these days
● 72% of online
adults use
social
networking sites

3. ● In Britain and the
US, approx 1 hour
a day on social media
● 90% of marketers use
social media channels
for business

4. Popularity of Social Networking Sites
Twitter
● 284 million monthly active users
● 100 million daily active users
● 80% of world leaders use Twitter
Facebook
● 1.35 billion monthly active users. 864 million
daily active, 10 billion messages a day
● 30% of Americans get their news from Facebook
● Facebook has more users than the whole of the
Internet did in 2005
● Google+: 300 million monthly active users
● LinkedIn: 332 million users
● MySpace: 36 million users
● 44% of “users” have
never sent a tweet
● 390 million users have
no followers
● Google+: only 7 minutes
per month

5. Your grandmother is three times as likely to
use a social networking site now as in 2009

6. Why analyse social media?
● Contrary to popular belief, Twitter isn't just full of tweets about
Justin Bieber.
● In an emergency, one in two people would use social media to
let people know they were safe or to find out more information
● Less than 24 hours after the recent Nepal trekking disaster hit,
Facebook and Twitter accounts had been set up to provide
information channels, missing persons register etc.
● For companies, sentiment analysis tools are critical to keep
track of the market pulse, customer feedback, etc.
● Fast-growing, highly dynamic and high volume source of data
● Reflects language and current views of today's society
● Analysing social media is far more efficient than e.g. youGov
polls

7. Opinion mining from social media
● Understanding customer reviews and so on is a huge business
● But also:
● Tracking political opinions: what events make people change
their minds?
● How does public mood influence the stock market, consumer
choices etc?
● How are opinions distributed in relation to demographics?
● Who are the opinion influencers?
● SMA tools are crucial in order to make sense of all the
information

8. Social media analysis for journalists
● Twitter is immensely valuable to news
professionals
● gauging opinion on breaking news
● discovering new stories
● first hand reports from disasters,
war zones, ...
● Issues of veracity: London Eye on Fire!

9. Analysing language in social media is hard
● Grundman:politics makes #climatechange scientific
issue,people don’t like knowitall rational voice tellin em wat 2do
● @adambation Try reading this article , it looks like it would be
really helpful and not obvious at all. http://t.co/mo3vODoX
● Want to solve the problem of #ClimateChange? Just #vote for a
#politician! Poof! Problem gone! #sarcasm #TVP #99%
● Human Caused #ClimateChange is a Monumental Scam!
http://www.youtube.com/watch?v=LiX792kNQeE … F**k yes!!
Lying to us like MOFO's Tax The Air We Breath! F**k Them!

10. We need tools for hashtag analysis
● Hashtags need unravelling:
● #gasprices
● And disambiguating:
● #therapist
● #nowthatcherisdead

11. Hijacking of hashtags

12. #earthhour2014

13. NER is dead! Long live NER!

14. NER on Tweets
● NER on Tweets much harder than on longer text
● Very short, so ambiguous terms hard to interpret
● Poor grammar and spelling, use of abbreviations, shorthands
● Twitter-specific features: hashtags, @mentions, etc.
● Tools designed for longer texts do very badly on Twitter
System P R F1 F0.5
OpenCalais 68.59 67.17 67.87 68.30
Lupedia 70.93 44.17 54.44 63.27
TextRazor 59.12 83.83 69.34 62.82
TwitIE 69.69 61.03 65.07 67.76
Zemanta 29.64 29.31 29.47 29.57

15. Tools for Sentiment Analysis
● There are lots of tools for sentiment analysis around
● Many of them don't work well at more than a very basic level
● They mainly use dictionary lookup for positive and negative words
● ML methods only works for text that's similar in style to the
training data, and it's hard to understand when it goes wrong
● Things like sarcasm tend not to get picked up
● They classify the tweets as positive or negative, but not with
respect to the keyword you're searching for
● keyword search just retrieves any tweet mentioning it, but not
necessarily about it as a topic
● no correlation between the keyword and the sentiment

16. Sentiment Analysis in GATE
● Knowledge-based linguistic approach based on entity
detection for opinion holders and targets
● Sentiment words have to be in a linguistic relation to the
opinion holder and target
● Use linguistic analysis to deal with scope issues
(negation, hashtags, sarcasm etc)
● Sentiment word scores are modified incrementally
● Easy understanding of errors and adaptation of the rules
● Twitter-specific pre-processing using TwitIE

17. This all sounds like it would be hard
to set up on my system!

18. GATE Cloud to the Rescue
● What?
● end-to-end text and web processing solutions from the
GATE family running on cloud computing infrastructures.
● Why?
● Solve any sort of text processing problem: web, text or
opinion mining; indexing and search (fulltext, boolean,
conceptual, structural); information extraction; semantic
annotation; sentiment analysis; ontology population; etc.
● Run large-scale jobs without investing in server hardware
or other fixed costs.
● Exploit a 15-year R&D programme, the expertise of the
GATE community and a defined and repeatable process.

19. Benefits of Gate Cloud
Text Analytics Consumer
Cloud Large scale, no CAPEX,
no system admin, no commitment
Open Source No vendor lock-in
TA Services Twitter, News, BioMed, Sentiment, etc.
low-level pre-processing support (POS tagging etc)
APIs Integrate

20. Application Types
● Low-level: stemmers, PoS taggers, phrase chunkers,
morphological analysers
● Coverage: tools for 18 languages including BG and RU
● General Purpose IE: named entities, numbers,
measurements, language ID
● Domain-specific IE: News, TwitIE, Biomed
● LOD-based semantic annotation: DBpedia, GeoNames,
Freebase
● Sentiment analysis
● Summarisation
● Includes many 3rd party tools also
20

21. On-demand document processing workflow

22. It's just like online shopping
● Click through to the online shop, browse products and add them to
your shopping basket.
● Create an account and then buy credit vouchers
● Put the vouchers in your account, and go to checkout.
● We'll email you the login or job creation details for your cloud servers.
● Monitor and control your cloud machines on your dashboard.
● Use our existing applications:
● Just upload your documents and sit down with a cup of tea
● Create your own pipeline:
● Upload your own customised application along with your
documents, and sit down with a cup of tea

23. 23
Summary
● SMA tools are crucial, but hard to find what's good
● Solutions are readily available in GATE
● Easy to test different versions and configurations
● Open source and easily customisable
● Big data and installation problems are solved with GATE Cloud:
● PaaS for text analytics
● Low barrier to entry
● Just pay for what you use
● State-of-the-art pipelines for news and social media
● More pipelines constantly being added

24. Acknowledgements and more information
● GATE: http://gate.ac.uk
● GATE Cloud: http://gatecloud.net
● Annomarket: http://www.annomarket.eu
● Research partially supported by the European Union/EU under
the Information and Communication Technologies (ICT) theme
of the 7th Framework Programme for R&D (FP7) DecarboNet
(610829) and AnnoMarket (296322)
● Original GATE Cloud development supported by JISC/EPSRC,
reference number EP/I034092/1
This document does not represent the opinion of the European Community, and the
European Community is not responsible for any use that might be made of its content