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State of the Union


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State of the Union

  1. 1. State  of  the  Union:  Social  Media  Report This  report  is  an  analysis  of  the  Internet  conversations  relating  to  the   State  of  the  Union  address.  We  analyzed  many  millions  of  messages   from  sources  such  as  Twitter,  Blogs,  Social  Networks,  news  sources   and  other  online  publications  to  provide  true  measurement  and   understanding  of  messages  that  are  relevant  to  this  study.   Our  analysis  covers  messages  and  articles  around  the  time  of  the  televised   address,  before  and  after  the  event  to  provide  a  detailed  look  at  this  chatter. Below  we  have  a  Trend  chart  that  shows  all  the  conversations  around  the  State  of  the  Union  address  and  what   percentage  of  that  conversation  was  dedicated  to  each  topic.  The  Y  axis  is  labeled  with  percentages.  This  is  our   normalized  Post  Reach:  This  value  is  the  total  number  of  post  results  matching  our  query  divided  by  the  total  number   of  results  for  the  State  of  the  Union. Here  we  see  that  economy,  spending  and  healthcare  were  the  top  three  topics  being  talked  about  before  the  State  of   the  Union.  This  could  mean  that  people  were  expecting  these  to  be  the  most  talked  about  topics  in  the  address.   The  days  following  the  speech  we  see  a  very  large  percentage  of  the  conversation  being  focused  on  the  economy  and   spending.  It's  also  interesting  to  note  that  healthcare  conversations  continued  to  fall  in  relation  to  other  State  of  the   Union  chatter.
  2. 2. Sentiment  Analysis  for  Jobs Analytics  is  a  powerful  tool  which  uses  automated  textual  analysis  (frequently  called  Natural  Language  Processing,  or   NLP)  to  determine  subject-­‐speciRic  sentiment  information,  topics  of  conversation  and  interesting  words  in  thousands   of  pieces  of  content  on  request  in  under  a  minute.  The  system  backing  Analytics  is  the  most  powerful  analysis  system   in  the  industry.   We  can  Rilter  results  based  on  the  queries  we  build.  This  means  that  we  can  actually  see  what  people  are  saying   speciRically  about  a  certain  topic.  In  this  instance  we  are  looking  at  the  sentiment  around  President  Obama  in  relation   to  jobs  and  unemployment.   Topic  Word  Cloud The  Rirst  box  we  see  is  a  “Topic  Word  Cloud”.  This  box  contains  hot  topics  of  conversation  within  the  articles  around   jobs.  By  default,  the  words  are  sized  based  on  how  important  the  system  believed  them  to  be  in  these  conversations   (larger  being  more  signiRicant)  and  colored  based  on  sentiment  /  tone  averages  used  with  that  topic.  If  a  topic  is   green,  it  is  generally  referred  to  positively  in  this  context.  If  it  is  red,  it  is  frequently  negative.  
  3. 3. Overall  Sentiment   Below,  we  see  two  pie  charts.  These  charts  show  the  overall  sentimental  tone  for  job-­‐related  conversations  in  relation   to  the  President.  The  left  side,  labeled  Sentiment  by  Subject  References,  shows  the  percentage  of  speciRic  references  to   jobs  which  were  positive,  negative  or  mixed  (mixed  being  those  which  were  both  positive  and  negative).  The  right   side,  labeled  Sentiment  by  Subject  Posts,  are  the  percentage  of  articles  or  posts  which  contained  sentiment  about  jobs   that  were  positive,  negative  or  mixed. Sentiment  Trend The  next  tool  we  see  is  our  sentiment  trend  which  shows  the  sentimental  tone  over  time.  We  can  see  some  big   spreads  early  in  September,  late  in  November,  and  again  in  late  December.  
  4. 4. Word  &  Category  Analysis Finally,  the  “Word  and  Category  Analysis”  shows  the  most  commonly  used  adjectives.  This  will  tell  us  what  percentage   of  the  posts  contained  these  adjectives  and  the  sentiment  behind  them.  We  also  see  in  the  last  column  a  list  of   categories  that  adjectives  fall  into.  This  is  helpful  to  see  the  context  of  the  sentimental  tone  and  how  much  of  this   content  falls  inside  these  categories.   Sentiment  Analysis  for  Spending Here  we  analyze  sentiment  around  government  spending.  We  can  see  in  the  word  cloud  below  that  Democrats  are   viewed  more  negatively  than  Republicans  when  it  comes  to  spending.  Also,  we  see  that  Bush  is  mentioned  as  well.  In   this  case  people  are  defending  President  Obama  by  reminding  others  that  President  Bush  had  large  budget  deRicits   and  over  spent  signiRicantly.  Below  analytics,  we  can  see  posts  that  illustrate  this  point.   The  sentiment  trend  has  not  changed  much  over  time  but  we  do  see  some  signiRicant  spreads  throughout  the  last  few   months.
  5. 5. Post  /  Article  Viewer Here  we  have  some  examples  of  the  posts  that  we  have  aggregated  into  our  database.  We  index  posts  as  they  appear   online.  The  posts  below  can  explain  some  of  the  sentiment  above.  Inside  the  tool  itself  we  are  able  to  click  the  blue   arrows  to  the  left  of  the  post  and  see  all  of  the  content.  
  6. 6. Online  InRluence  Map Top  Sources  is  a  powerful  tool  which  is  rather  unique.  The  data  generated  by  this  process  can  be  viewed  either  as  a   list  or  in  an  interactive  visualization  map.   In  this  instance  we  Rind  the  most  inRluential  websites  talking  about  President  Obama  over  the  last  year  and  a  half.  This   can  be  beneRicial  if  you  wanted  to  get  an  inRluential  third  party  blog  to  write  a  favorable  story  about  an  issue.  The   ecosystem  below  contains  the  top  100  online  sources  using  Social  Radar's  Top  Sources  Algorithm.  Top  sources  are   determined  both  by  the  amount  of  inRluence  of  the  source  and  other  factors  such  as  the  amount  of  relevant  posts. Each  circle  represents  a  source,  and  each  line  represents  a  link.  You  can  bind  the  size  and  color  of  the  circles  to   different  attributes  to  help  you  in  determining  inRluence  and  activity.