Social Data Sentiment Analysis

Social  Sentiment  Analysis  in  Social  Data  

Seth  Grimes  Alta  Plana  Corporation  

@sethgrimes  

Social  Data  and  Analytics  –  DC  March  11,  2015  

Social  Sentiment  Analysis  in  Social  Data  

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http://altaplana.com/TA2014  

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News  in  the  last  week…  

Social  Sentiment  Analysis  in  Social  Data  

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Social  Sentiment  Analysis  in  Social  Data  

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technewstoday.com  

Social  Sentiment  Analysis  in  Social  Data  

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Social  Sentiment  Analysis  in  Social  Data  

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Social  Data?  •  Profiles.  •  Identity,  categories.  

•  Connections.  •  Elements:  Directionality,  degree.  

•  Content:  Text,  photos,  videos,    •  Actions:  Likes,  Clicks,  Shares…  •  Elements:  Time,  location,  target,  sequence.  

 Sentiment  can  be  extracted  or  inferred  from  all  of  these.  How?  

Social  Sentiment  Analysis  in  Social  Data  

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Analytics  is  the  systematic  application  of  algorithmic  methods  that  derive  and  deliver  information,  typically  expressed  quantitatively,  whether  in  the  form  of  indicators,  tables,  visualizations,  or  models.  •  Systematic  means  formal  &  repeatable.  •  Algorithmic  contrasts  with  heuristic.  

Analytics  creates  and/or  applies  models.  

Social  Sentiment  Analysis  in  Social  Data  

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Sentiment  –  •  Opinion,  attitude,  emotion,  and  mood:  

Affective  states.  •  Communicated  via  expressions  and  actions.  •  Understood  contextually.  •  Applied  situationally.  

Anyone  who  tells  you  that  sentiment  analysis  is  solely  about  computing  a  positive/negative/neutral(/mixed)  evaluation  is  probably  hawking  a  weak  solution.  

Social  Sentiment  Analysis  in  Social  Data  

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The  sentiment  value  of  an  opinion  may  be  expressed  as  a  quintuple  (oj,  fjk,  soijkl,  hi,  tl):  •  oj  is  a  target  object.    •  fjk  is  an  feature  of  the  object  oj.  •  hi  is  an  opinion  holder.    •  tl  is  the  time  when  the  opinion  is  expressed.    •  soijkl  is  the  sentiment  value  of  the  opinion  of  the  opinion  holder  hi  regarding  feature  fjk  of  object  oj    at  time  tl.    

•  soijkl  is  +ve,  -­‐ve,  or  neu,  or  a  more  granular  rating.    

Bing  Liu,  NLP  Handbook  

Social  Sentiment  Analysis  in  Social  Data  

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Comparative  opinions  -­‐-­‐      (O1,  O2,  F,  po,  h,  t):    •  O1  and  O2  are  object  sets  being  compared  based  on  shared  features  F.  

•  po  is  the  preferred  object  set  of  the  opinion  holder  h.  

•  t  is  the  time  when  the  comparative  opinion  is  expressed.  

 Bing  Liu  

Social  Sentiment  Analysis  in  Social  Data  

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Social  Sentiment  Analysis  in  Social  Data  

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Social  Sentiment  Analysis  in  Social  Data  

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Social  Sentiment  Analysis  in  Social  Data  

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Questions  for  business  (&  government):  What  are  people  saying?    What’s  hot/trending?  What  are  they  saying  about  {topic|person|product}  X?  ...  about  X  versus  {topic|person|product}  Y?  How  has  opinion  about  X  and  Y  evolved?  How  has  opinion  correlated  with  {our|competitors’|general}  {news|marketing|sales|events}?  

Who  (and  What,  When  &  How)  are  opinion  leaders?  How  does  sentiment  propagate  across  multiple  channels?  What’s  behind  opinion,  the  root  causes?  

(How)  Can  we  link  opinions,  profiles,  behaviors  &  transactions  to  discern  intent  and  predict  actions?  

Social  Sentiment  Analysis  in  Social  Data  

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Measurement  methods  –  •  Polling,  surveys  including  NPS.  •  Natural  language  processing.  •  Action  stats.  •  Network  and  information-­‐flow  analysis.  •  Biometrics.  

Modelled  concepts  –  •  Affinities  (clusters).  •  Influence,  Authority.  •  Satisfaction,  Loyalty,  Motivation.  •  Likelihood  to  buy,  Churn  propensity...  

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Current, 33%

Current, 31%

Current, 34%

Current, 47%

Current, 51%

Current, 56%

Current, 47%

Current, 54%

Current, 66%

Expect, 21%

Expect, 24%

Expect, 23%

Expect, 23%

Expect, 28%

Expect, 25%

Expect, 33%

Expect, 28%

Expect, 22%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Events  

Semantic  annotations  

Other  entities  –  phone  numbers,  part/product  numbers,  e-­‐mail  &  street  addresses,  etc.  

Metadata  such  as  document  author,  publication  date,  title,  headers,  etc.  

Concepts,  that  is,  abstract  groups  of  entities  

Named  entities  –  people,  companies,  geographic  locations,  brands,  ticker  symbols,  etc.  

Relationships  and/or  facts  

Sentiment,  opinions,  attitudes,  emotions,  perceptions,  intent  

Topics  and  themes  

Do  you  currently  need  (or  expect  to  need)  to  extract  or  analyze...  

http://altaplana.com/TA2014  

Social  Sentiment  Analysis  in  Social  Data  

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Many  options  (text).  

Social  Sentiment  Analysis  in  Social  Data  

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Emotion  and  outcomes  

Social  Sentiment  Analysis  in  Social  Data  

Seth  Grimes  Alta  Plana  Corporation  

@sethgrimes  

Social  Data  and  Analytics  –  DC  March  11,  2015