Text Mining Yesenia Gonzalez Pearson Ing

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    Text Mining Yesenia Gonzalez Pearson Ing - Presentation Transcript

    1. TextMiningTheLastword
      Yesenia González Pedraza
    2. Text Mining
      Recent studies indicate that 80% of the information in a company is stored as text.
      TextMiningfocuses in discoveringamong a largequantity of textual information:
      Partnerships
      Trends
      Deviations
    3. Open Questions…
      ¿why do yousaythatyou
      liketheproduct?
      Insist and go in depth
      ¿what do yourememberseeing?
      Ifyouwerethe manager
      ¿whatwouldbethefirstthingyou’d
      tomakeyourcostumers
      More satisfied?
    4. Riesgos al codificar
      RISKS WHEN CODIFYING
      Mediation of thecoder:
      Totheintervention of thepolltheone of thecoderisaddedwhomust decide and interpret.
      Impoverishment of thecontent: Whentheallowsanswers of greatdiversity- complexsurveys, contradictoryor vague- theinformationisconfused.
      Thelittlefrequentanswers are eliminated a priori.Therare, original and unclearanswersoriginated in thefirstreading are assignedto residual codes.
      Destruction of theform: theform of theinformationismutilated and oftenitscontentbecomesimpoverished.
    5. TextMining
      ¿Whoistalking?
      ¿Whatis he saying?
      I’dinstall air conditioningand besidesthatI’dimplementappoinmentsbyphonetohavefasterattention.
      Youshouldpay more attentionwhenassitingthecostumerbecause
      wesometimeshavetowaitforhours.
      Modifythestoretobe more satisfied in thewaitingroom,respecttheturns.
      Theyoughttohave parking because are cars are towedaway
      DO NOT discriminatepeoplethat are notwearing a suit.
      Betterventilationbecausethe place istoosmall.
    6. ¿What is the costumer saying?
      satisfaction
      AMIABILITY
      SMILE
      SPEED
      comunication
      clarity
      PROXIMITY
      comfort
      NO
      IDEAL
      help
      EASINESS
    7. APPLYING TEXT MINING TO
      MARKET RESEARCH
      Yesenia González Pedraza
    8. ¿How can we apply text mining to market research?
      Identifying specific vocabulary by segments
      Find the words more frequently used in each group and the context of them.
      Identifythespectrum of thewordsyourclients use.
      Revise thecodification of the open questions.
      Communicate with your costumers using their language
      Rapidly find phrases wrongly classified
    9. ¿How can we apply text mining to market research?
      Finding the structure of word association in a text
      Graphicallyvisualizetheassociation of wordswithdemographicsegments, bahavior, etc.
      Identifyvocabularyzones
      Verifyifthereisprogression of thelanguage
      As your costumers get older,they use different words to communicate the same idea..¡Learn them!
      Find the words of predominant usage feminine or masculine
    10. IDENTIFYING SPECIFIC VOCABULARY BY SEGMENTS
      Yesenia González Pedraza
    11. Identifying Vocabulary
      SemanticFields
      Wordsthatcharacterizethesegmentsbyeithertheirpresenceorabsence.
      • In the top there are the most frequently found words and segments in each group. and (positive particularity) .
      • At the end of the list we find the least repeated words or segments found with less frequency (negative particularity)
      In this stage we find the repeated words and repeated segments by profiles allowing to recognize specific vocabulary.
      A,B,C
    12. Identifying Vocabulary
      • If you were the manager… ¿ What would you do to make your costumers feel more satisfied?
      • Question asked to costumers in a household store chain.
    13. Vocabulary C+
      Featurewords of thesegment
      Internpercentage
      Global percentage
      Internfrequencies
      Test value
      Global frequencies
      20 11 70 192 5.345
      14 7 48 126 4.702
      6 2 22 42 4.578
      30 20 104 354 4.323
      7 3 23 53 3.806
      Positive particularity
      PARKING
      CUSTOMIZED
      GIVE
      NEW
      PREPARED
      TYPICAL PHRASES
      “Give specialized attention”
      Ind. 3455
      SPEED
      MONEY
      OPEN
      PEAK
      WOULD HIRE
      MOOD
      EVITAR
      Negativeparticularity
      “The staff needs to be kind and gives clear information about all the products”Ind 5677
    14. Vocabulary D
      Featurewords of thesegment
      Internpercentage
      Global percentage
      Internfrequencies
      Test value
      Global frequencies
      Positive particularity
      52 41 355 727 5.228
      15 10 100 178 4.497
      1.8 1.6 1265 2959 3.938
      3 1 18 23 3.598
      26 21 178 369 3.468
      2 1 15 19 3.292
      44 38 305 670 3.288
      GOOD
      ASSIT THEM
      NO
      TREAT THEM
      AMIABLITY
      SPEED
      MONEY
      TYPICAL PHRASES
      FOLLOW UP
      PRODUCTS
      SALESMAN
      PRICE
      AFFABILITY
      CHANALIZED
      CHANGE
      “Faster service and more affability”
      Ind. 275
      Negativeparticularity
      “I’d hire more staff” Ind 1293
    15. The context of “NOT”
      BE HONEST ABOUT HOW MUCH THEY ARE GOING TO CHARGE TO
      BE FOOLISHED
      THAT THE CONTACT WITH COSTUMER CARE BE MORE PERSONAL AND
      BY PHONE
      NOT
      TREAT COSTUMER EQUALLY WELL EVEN IF THEY ARE
      WEARING A SUIT
      THAT THEY ARE MORE ACTIVE WHEN ASSITING SOMEBODY AND THAT THE STORE DOES
      SMELL BAD
      HAVE TO WAIT
      OPEN MORE PLACES OR SPOTS SO THAT COSTUMERS WOULD
      PREFER PEOPLE THE STAFF IS ACQUAINTEDWITH
      THAT THERE’S EQUALLITY AND THAT THEY DO
    16. RECOMMENDATIONS
      • The clients in level C + need an extra in the service of the chain. They need their purchase to be more comfortable generating actions that cutomise the service .
      • Level D percieves descrimination. They need to be taken care of, to be listened to and to be considered just like anyone else.
    17. FINDING THE STRUCTURE OF WORD ASSOCIATION IN A TEXT
      Yesenia González Pedraza
    18. Structure of Association
      Theanalysis of correpondencesallowstoidentifyvocabularyzones in particular thosecharacterizedbysomeprofile : women, youngpeople, men of highlevel.
      Theassociationbetweenthe use of certainwords and thecharacteristics of theindividualsinterviewedismade visible immediately in thistype of representation .
      Findinfthesevocabularyzonesallowstocarryoutlaterdeeperexplorations .
    19. Structure of Association
      • Extracted from “Health in the cities” destined to point out life habits related to health.
      • ¿What is Health for you?
    20. Structure of Association
      He can
      less
      few
      one
      each
      To go
      sea
      bad
      Be careful
      problem
      mentally
      M+50
      general
      normal
      already
      he can
      health
      can
      We have
      To drink
      ejercicio
      To do
      things
      work
      To have
      as
      To be
      principal
      all
      none
      well
      M-35
      form
      There is
      better
      very
      is
      when
      F-50
      M-20
      not
      sport
      smoke
      same
      well
      To have
      sick
      M-50
      something
      I
      do
      nothing
      eat
      that
      fisics
      I have
      bad
      because
      F+50
      body
      Tobe
      feel
      find
      I am
      having
      happy
      life
      disease
      your
      work
      good
      You have
      feel
      fisically
      for
      It gets
      F-20
      fisic
      diseases
      healthy
      well
      state
      F-35
      A mi
      well
      mental
      aches
      bring
      any
      psychic
      I think
      money
      too
      their
      but
      my
      he
      feeding
      pains
      You can
      Find yourself
      desires
      now
      pain
      Psychically
    21. CONCLUSIONS
      • The observed configuration suggets the existence of a progressive evolution of the vocabulary as you grow older.
      • There is a phase angle between genders ; the transition towards the use of certain words becomes to different ages, at an early age for women.
      • There is a use of words differentiated by sex .
    22. ADVANTAGES IN
      TEXT MINING
      Yesenia González Pedraza
    23. fast
      Less mistakes
      OPEN QUESTIONS
      No information
      loss
      Greater depth
      Yesenia González Pedraza
      Actionable
      Elements
    24. TextMiningisalsoused in…
      • Segmentation studies
      • Concept evaluation
      • Advertising recollection
      • Blogs monitoring
      • Focus groups
    25. THANK YOU VERY MUCH
      Homero 223-P.H.
      Col. Polanco CP 11560 México, D.F.
      Tel. (+52 55) 55 31 53 24, (+52 55) 55 31 55 60. Fax. (+52 55) 52 03 82 30
      pearson@pearson-research.com
      www.pearson-research.com

    + Yesenia González PedrazaYesenia González Pedraza, 1 month ago

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