Descubriendo información comercial desde la web social.€¦ · Descubriendo información comercial desde la web social. Sebastián A. Ríos Departamento de Ing. Industrial ... personas

Descubriendo información comercial desde la web social.

Sebastián A. RíosDepartamento de Ing. Industrial

[email protected]://wi.dii.uchile.cl

Thursday, January 6, 2011

mailto:[email protected]

mailto:[email protected]

http://wi.dii.uchile.cl

http://wi.dii.uchile.cl

Outline

• Motivación

• Social Web Sites

• Problemas y Soluciones

• Aplicaciones y Ejemplos


Motivación


SOY DIGITAL 2010 Reporte sobre experiencia de consumo digital en Chile. 30

A partir de la asignación de valor que dan las personas a elementos de la interfaz que ayudan a tomar decisiones de compra, vemos que los “comentarios de los usuarios” conservan el primer lugar, asimismo el tercer lugar se lo llevan “las descripciones de un blogger o medio independiente”, lo que nos da luces claras de cómo la integración de información que no sale de la tienda, es un factor relevante para sentar las condiciones que promuevan una experiencia positiva en los consumidores.

Los factores que inciden en una decisión de compra

77,5%71,7%

59,9%

51,7%

43.4%

41.1%

24.9%

24.1%

IMÁGENES

VOTOS DE LOS USUARIOS

RANKING PRODUCTOS MÁS DEMANDADOS

DESCRIPCIÓN PROPIA DEL SITIO

COMENTARIOS DE LOS USUARIOS

VIDEOS

DESCRIPCIÓN DE UN BLOGGER O MEDIO INDEPENDIENTE

COMPARACIÓN CON OTROSPRODUCTOS SIMILARES

Gráfico 07: 77,5% de las personas valoran los comentarios de otros usuarios como referentes de aprobación para tomar una decisión de compra

Más aún, cuando el 58,1% de las personas declara comentar en las redes sociales y un 47,9% recomienda sitios, videos o aplicaciones a sus amigos, lo que nos habla de una creciente tendencia hacia la participación y construcción de relaciones de influencia, enalteciendo el rol activo que están tomando las personas en el consumo.

Tus consumidores ya se anticiparon (y en tiempo real)Los usuarios son más ágiles y proactivos que lo que las marcas consideran, tienen una relación con los productos segundo a segundo que dura mucho más tiempo que los tradicionales 30 segundos de un anuncio. Así que probablemente la gran mayoría de los productos tecnológicos y artículos electrónicos disponibles en tiendas online tradicionales, ya hayan sido revisados, descritos, calificados y comentados por alguna persona o medio independiente, lo que implica que actualmente las interfaces para apoyo a la decisión de compra son altamente infecientes, en tanto las personas tienen que

CAUTIVACIÓN Y ESTÍMULO DE DECISIÓN

Fuente [2]


Usuarios chilenos investigan en Internet antes de comprar [1].

“Con 77,5% de las menciones, los comentarios de otros usuarios sobre el producto y la comparación de éstos en otros sitios son los factores determinantes para concretar la compra, incluso para aquellas que se realizan en el comercio tradicional.”


Social Web Sites


• Sitio Web donde las personas se pueden compartir ideas, comentarios, experiencias, sentimientos, proyectos, etc.

¿Qué es un Social Web Site?


Basicamente...


!"#$%&'()*$%'Basicamente...


PlataformasSociales

Blogging

PhotoShearing Micro

Blogging

RSS

Widgets

SocialNetworking

ChatRooms

MessageBoards

Podcasts

VideoSharing

¿Donde estan estas conversaciones?


!"#$%&'()*+,-$,&'./&*/

01121112111'3

4$&"&/')5'")6#&6#'/78*&,'9&*':)6#7

;0211121112111

<)"8+'=>/$6&//'-$#7'8"#$%&'?8"&=))@'98A&/

B20112111

Fuente [3]


!"#$%&&'#()*+,#-')*+.%/0#"1

2#(/)'(&+3"#$'*%&+#$+4#"/5(%+677+%8%-5/'9%&+)(:+*%):'(;+%(/"%3"%(%5"&

<9%");%+'(:'9':5)*+&)*)"=+#(+>'(1%:?(+'&+@A7BC777+

Fuente [3]


!"#"$%&'"(#

)*$+,(-(./012")$34"5),,"()

67$-/'$8141$0)"2$9:$7/4$1;14-$)45"(.1$0/*512$/,

-/'$8/'.2$1)4,$9:<=:>?@>

A+B$CDEBFuente [3]


!"#$%&"'"()*%"'%

+,-.///.///)0

*1(2&"%)1"')345&6

,7.///.///)0

*%"'%)84994:$5#)2);'25(

<+=

>'25()'"?433"5(2&$45%)1"')345&6

@.A//.///)0

Fuente [3]


Problemas y Soluciones


...y...¿cómo sacamos provecho a la web

social?


¿Cómo obtener las preferencias de las personas desde la

Web Social?


Encuestas y Estadísticas

• Encuestas usando social web sites

• Posteriormente obtener estadísticas y otra información valiosa a partir de ellas.


... veamos las estrellitas!


Reputación Web

Este producto falla Poco

Buena Imagen

Los productos son buenos

La Atención al Cliente es Excelente

Good Karma!


... y las preferencias?

... que las estrellitas tienden siempre a ser positivas. Pormedio en YouTube (4,8 out of 5 in the

old interface)[4]

...estudios demuestran que es difícil obtener datos de buena calidad por

encuestas online


Problemas!

¿Cómo obtener las preferencias de las personas desde la web social?

¿Cómo usar esta información para mejorar nuestros productos y servicios?


¿Cuál es la mejor manera de hacer esto?

Lo mejor es poder extraer la información que necesitamos directamente desde los

comentarios!

Fácil de decir, pero difícil de hacer!


¿Cómo se puede hacer?

Aplicación de Técnicas de Web Intelligence y de Análisis de

Redes Sociales [9]

Web Text Mining

Web Structure Mining

Web Usage Mining

Social Network Analysis

+

Desafío: Fácil de decir, pero difícil de hacer


Ventajas1. Se extrae lo que los usuarios realmente

opinan

2. Es posible obtener la encuesta más grande que se quiera.

3. Es posible hacer estudios evolutivos, prácticamente con el mismo esfuerzo de desarrollo

Entre otras...


Lo malo...

Privacidad

Ataques


Preferencias

Blogging

PhotoShearing Micro

Blogging

RSS

Widgets

SocialNetworking

ChatRooms

MessageBoards

Podcasts

VideoSharing

1. Se puede obtener información de muchas de

estas plataformas2. Se puden cruzar datos

inter-plataforma


Aplicaciones y Ejemplos


Análisis Sobre Reclamos


Objetivo

Poder encontrar conocimiento valioso respecto de los productos y servicios de las

empresas de retail usando los reclamos de las personas en sitios web sociales


Objetivos Especificos1. Encontrar familias o tipos de reclamos

2. Encontrar tendencias en los reclamos

3. ¿Cómo evolucionaron los reclamos de las personas? (los problemas persisten, con que frequencia)

4. Identificar redes sociales afectadas por ciertos reclamos específicos

5. Conectar y buscar relaciones implicitas en redes inconexas

6. Intentar medir el impacto de los reclamos mediante el analisis de estas redes sociales


...qué queda fuera?

Medidas Correctivas


Valor Fundamental!

Preservaranonimato de las

personas y empresas


¿Como se reclama hoy?

Mail directo a las empresas

Mail a Organismos Fizcalizadores

Publicación en Portal de las empresasPublicación en Sitios de Reclamos

...independiente si los reclamos son resueltos o no!


Datos Utilizados(porcion) Reclamos de

cuatro retail entre el 2006 y 2010

Obtenidos desde un Foro de Reclamos

Retail A

Retail B

Retail C

Retail D

N. de Comentarios: 310 709 242 1125


1. Encontrar familias o tipos de reclamos

Dos Alternativas:

A. Usar Text Mining

B. Usar Modelos de Topicos/Conceptos

Enfoque tradicional, usando vecotores de palabras y luego clusterizando e indexando por palabras clave

Avanzado, utiliza distribuciones de probabilidad sobre el texto para generar temas/conceptos de manera automática


Por que A no sirve

Retail A

Retail B

Retail C

Retail D


Tamaño Vectores Pal: 6271 12199 6931 15934


¿Qué significa ésto?HomeThe pages of a web site are represented as

a vector of words with some frequency. Later, this vectors are used by a clustering or classification algorithm in order to find some interesting patterns.

School

Engineering

University

Student

Course

Lecture

Department

Alcohol

0.0002604

0.0002234

0.0001225

0.0000023

0.0234545

0.0002667

0.1093356

0.0000580

A Web Page

Thousands of components(experimentaly 5.000

components per web page) Analyst

???

TFIDF


Modelos de Topicos

text mining. This way, we can discover threatening topics,

and perform specific analysis on each one.

3.1 Basic NotationLet us introduce some concepts. In the following, let V

a vector of words that defines the vocabulary to be used.

We will refer to a word w, as a basic unit of discrete data,

indexed by {1, ..., |V|}. A post message is a sequence of Swords defined by w = (w1, ..., wS

), where wsrepresents the

sthword in the message. Finally, a corpus is defined by a

collection of P post messages denoted by C = (w1, ...,w|P|).

A vectorial representation of the posts corpus is given by

TF-IDF= (mij), i ∈ {1, . . . , |V|} and j ∈ {1, . . . , |P|} , where

mij is the weight associated to whether a given word is more

important than another one in a document. The mij weights

considered in this research are defined as an improvement of

the tf-idf term [26] (Term Frequency times inverse documentfrequency), defined by

mij = fij(1 + sw(i))× log

„|C|ni

«(1)

where fij is the frequency of the ith word in the jthdoc-

ument, sw(i) is a factor of relevance associated of word iin a set of words, and ni is the number of documents con-

taining word i. In this case, sw(i) =wi

post|P| , where wi

post

is the frequency of word i over all documents, and |P| is

the total amount of posts. The tf-idf term is a weighted

representation of the importance of a given word in a doc-

ument that belongs to a collection of documents. The termfrequency (TF) indicates the weight of each word in a doc-

ument, while the inverse document frequency (IDF) states

whether the word is frequent or uncommon in the document,

setting a lower or higher weight respectively.

3.2 Topic ModelingA topic model can be considered as a probabilistic model

that relates documents and words through variables which

represent the main topics infered from the text itself. In

this context, a document can be considered as a mixture of

topics, represented by probability distributions which can

generate the words in a document given these topics. The

infering process of the latent variables, or topics, is the key

component of this model, whose main objective is to learn

from text data the distribution of the underlying topics in a

given corpus of text documents.

A main topic model is the Latent Dirichlet Allocation

(LDA) [3, 4, 9]. LDA is a Bayesian model where latent

topics of documents are infered from estimated probabil-

ity distributions over the training dataset. The key idea of

LDA, is that every topic is modeled as a probability distri-

bution over the set of words represented by the vocabulary

(w ∈ V), and every document as a probability distribution

over a set of topics (T ). These distributions are sampled

from multinomial Dirichlet distributions.

As described by [4], the latent Dirichlet allocation model

can be represented as a probabilistic generative process de-

scribed by the following sequence of events,

1. Choose a number of multinomial S (S ∼ Poisson(ξ))which represents the amount of words in a given mes-

sage.

2. Choose θ ∼ Dir(α).

3. For each ws ∈ w.

(a) Choose a topic zs ∼ Multinomial(θ).

(b) Choose a word wsfrom p(ws|zs, β), a multinomial

probability conditioned on the topic zs.

where the final set of topics T is built by the top k topics

zs of n words, for which k and n must be defined a-priori in

the experimental setup.

For LDA, given the smoothing parameters β and α, and

a joint distribution of a topic mixture θ, the idea is to de-

termine the probability distribution to generate from a set

of topics T , a message composed by a set of S words w(w = (w1, ..., wS

)),

p(θ, z,w|α, β) = p(θ|α)

SY

s=1

p(zs|θ)p(ws|zs, β) (2)

where p(zs|θ) can be represented by the random variable

θi, such that topic zs is presented in document i (zis = 1).

A final expression can be deduced by integrating equation 2

over the random variable θ and summing over topics z ∈mathcalT . Given this, the marginal distribution of a mes-

sage can be defined as follows:

p(w|α, β) =

Zp(θ|α)

SY

s=1

X

zs∈T

p(zs|θ)p(ws|zs, β)

!dθ (3)

The final goal of LDA is to estimate previously described

distributions to build a generative model for a given cor-

pus of messages. There are several methods developed for

making inference over these probability distributions such as

variational expectation-maximization [4], a variational dis-

crete approximation of equation 3 empirically used by [28],

and by a Gibbs sampling Markov chain Monte Carlo model

[8] which have been efficiently implemented and applied by

[19].

3.3 Network ConfigurationTo build the social network, the members’ interaction

must be taken into consideration. In general, members’ ac-

tivity is followed according to its participation on the forum.

Likewise, participation appears when a member post in the

community. Because the activity of the VCoI is described

according members’ participation, the network will be con-

figured according to the following: Nodes will be the VCoI

members, and arcs will represent interaction between them.

How to link the members and how to measure their interac-

tions to complete the network is our main concern.

In this work, will be describe two VCoIs’ network repre-

sentation according the following replying schema of mem-

bers:

1. Creator-oriented Network: When a member create

a thread, every reply will be related to him/her.

2. Last Reply-oriented Network: Every reply of a

thread will be a response of the last post.

In figure 5 the latter two approaches of network conver-

sion of the forum is presented. In figure 5, arcs represents

members’ replies and nodes represent the users who made

the posts. In our first approach, the weight of arcs will be a

text mining. This way, we can discover threatening topics,

and perform specific analysis on each one.

3.1 Basic NotationLet us introduce some concepts. In the following, let V

a vector of words that defines the vocabulary to be used.

We will refer to a word w, as a basic unit of discrete data,

indexed by {1, ..., |V|}. A post message is a sequence of Swords defined by w = (w1, ..., wS

), where wsrepresents the

sthword in the message. Finally, a corpus is defined by a

collection of P post messages denoted by C = (w1, ...,w|P|).

A vectorial representation of the posts corpus is given by

TF-IDF= (mij), i ∈ {1, . . . , |V|} and j ∈ {1, . . . , |P|} , where

mij is the weight associated to whether a given word is more

important than another one in a document. The mij weights

considered in this research are defined as an improvement of

the tf-idf term [26] (Term Frequency times inverse documentfrequency), defined by

mij = fij(1 + sw(i))× log

„|C|ni

«(1)

where fij is the frequency of the ith word in the jthdoc-

ument, sw(i) is a factor of relevance associated of word iin a set of words, and ni is the number of documents con-

taining word i. In this case, sw(i) =wi

post|P| , where wi

post

is the frequency of word i over all documents, and |P| is

the total amount of posts. The tf-idf term is a weighted

representation of the importance of a given word in a doc-

ument that belongs to a collection of documents. The termfrequency (TF) indicates the weight of each word in a doc-

ument, while the inverse document frequency (IDF) states

whether the word is frequent or uncommon in the document,

setting a lower or higher weight respectively.

3.2 Topic ModelingA topic model can be considered as a probabilistic model

that relates documents and words through variables which

represent the main topics infered from the text itself. In

this context, a document can be considered as a mixture of

topics, represented by probability distributions which can

generate the words in a document given these topics. The

infering process of the latent variables, or topics, is the key

component of this model, whose main objective is to learn

from text data the distribution of the underlying topics in a

given corpus of text documents.

A main topic model is the Latent Dirichlet Allocation

(LDA) [3, 4, 9]. LDA is a Bayesian model where latent

topics of documents are infered from estimated probabil-

ity distributions over the training dataset. The key idea of

LDA, is that every topic is modeled as a probability distri-

bution over the set of words represented by the vocabulary

(w ∈ V), and every document as a probability distribution

over a set of topics (T ). These distributions are sampled

from multinomial Dirichlet distributions.

As described by [4], the latent Dirichlet allocation model

can be represented as a probabilistic generative process de-

scribed by the following sequence of events,

1. Choose a number of multinomial S (S ∼ Poisson(ξ))which represents the amount of words in a given mes-

sage.

2. Choose θ ∼ Dir(α).

3. For each ws ∈ w.

(a) Choose a topic zs ∼ Multinomial(θ).

(b) Choose a word wsfrom p(ws|zs, β), a multinomial

probability conditioned on the topic zs.

where the final set of topics T is built by the top k topics

zs of n words, for which k and n must be defined a-priori in

the experimental setup.

For LDA, given the smoothing parameters β and α, and

a joint distribution of a topic mixture θ, the idea is to de-

termine the probability distribution to generate from a set

of topics T , a message composed by a set of S words w(w = (w1, ..., wS

)),

p(θ, z,w|α, β) = p(θ|α)

SY

s=1

p(zs|θ)p(ws|zs, β) (2)

where p(zs|θ) can be represented by the random variable

θi, such that topic zs is presented in document i (zis = 1).

A final expression can be deduced by integrating equation 2

over the random variable θ and summing over topics z ∈mathcalT . Given this, the marginal distribution of a mes-

sage can be defined as follows:

p(w|α, β) =

Zp(θ|α)

SY

s=1

X

zs∈T

p(zs|θ)p(ws|zs, β)

!dθ (3)

The final goal of LDA is to estimate previously described

distributions to build a generative model for a given cor-

pus of messages. There are several methods developed for

making inference over these probability distributions such as

variational expectation-maximization [4], a variational dis-

crete approximation of equation 3 empirically used by [28],

and by a Gibbs sampling Markov chain Monte Carlo model

[8] which have been efficiently implemented and applied by

[19].

3.3 Network ConfigurationTo build the social network, the members’ interaction

must be taken into consideration. In general, members’ ac-

tivity is followed according to its participation on the forum.

Likewise, participation appears when a member post in the

community. Because the activity of the VCoI is described

according members’ participation, the network will be con-

figured according to the following: Nodes will be the VCoI

members, and arcs will represent interaction between them.

How to link the members and how to measure their interac-

tions to complete the network is our main concern.

In this work, will be describe two VCoIs’ network repre-

sentation according the following replying schema of mem-

bers:

1. Creator-oriented Network: When a member create

a thread, every reply will be related to him/her.

2. Last Reply-oriented Network: Every reply of a

thread will be a response of the last post.

In figure 5 the latter two approaches of network conver-

sion of the forum is presented. In figure 5, arcs represents

members’ replies and nodes represent the users who made

the posts. In our first approach, the weight of arcs will be a

La finalidad de estos modelos es estimar alguna distribución de probabilidad

LDA Method

De este modo se encuentran conjuntos de terminos que con alta probabilidad ocurren simultaneamente.

Estos son llamados Topicos/Conceptos


Resultados LDA sobre los Reclamos

Topic 1th: Intereses Excesivos.Cobros

Inadecuados.Ingreso a DICOM

cuenta 0.031379 deuda 0.022933 XXXX 0.020842 pagar 0.020600 mes 0.018268 pago 0.016739

meses 0.012235dicom 0.006282 fecha 0.006202

cobranza 0.006121...40 terminos más...

Topic 2th: Problemas en la Garantia y Garantía

Extendida

servicio 0.029304 tecnico 0.026330 garantia 0.026220

producto 0.024568 XXXX 0.023247 compre 0.018071 tienda 0.017741 cambio 0.015318 equipo 0.012675

problema 0.010363 falla 0.007830

...39 terminos más ...


2. Encontrar tendencias en los reclamos

• Se obtubieron 20 topicos para los datos de los Retail A, B, C y D en conjunto

Retail A

Retail B

Retail C

Retail D


Tamaño Vectores Pal: 6271 12199 6931 15934

Topicos: 17 17 11 16


Intersección?

• RECLAMOS POR ALTOS INTERESES PRODUCTO DEL NO PAGO DE LA TARJETA/INGRESO A DICOM.

• RECLAMOS POR ERRORES EN LA PUBLICIDAD PUBLICADA. NO SE HACE EL DESCUENTO O NO EXISTE EL DESCUENTO O PROMOCIóN.

• RECLAMO POR MAL TRATO POR PARTE DE LOS GUARDIAS DE SEGURIDAD


3. ¿Cómo evolucionaron los reclamos de las personas? [6]Fuzzy ClusteringBasado en la Regla de Nakanishi para la Composición difusa [Nakanishi’93]

Sea R(U , V ) & S(V , W ) dos relaciones difusas que comparten un conjunto común V

Sea μR (x , y ) & μS (y , z ) funciones de pertenencia respectivas

Entonces:

μR◦S = ⋁{μR (x , y ) ∧ μS (y , z )}

Donde ⋁ es la suma limitada, Sum = min(1, x + y )

y ∧ es el producto algebraico, prod = x y


Ejemplo 1

Ejemplo: para retail D desde el año 2006 a Dic 2010

TOPIC 1TH: RECLAMO POR MALA CALIDAD DE IMAGEN EN TVS LCDTOPIC 2TH: RECLAMO POR DAñO EN CAMAS/COLCHONESTOPIC 3TH: NO HAY RESPUESTA EN EL SERVICIO TELEFóNICO

Esto también es buen indicador del impacto de un reclamo en el tiempo


Ejemplo 2

TOPIC 3TH: NO HAY RESPUESTA EN EL SERVICIO TELEFóNICOTOPIC 4TH: MALA ATENCIóN AL CLIENTE EN LA ATENCIóN TELEFóNICA.TOPIC 6TH: RECLAMO POR EMBARGO DE BIENESTOPIC 7TH: RECLAMO FRAUDES CON TARJETAS DE RETAIL/PERDIDA DE TARJETAS.



Redes Topicas - Por Reclamos

TOPIC 3TH: NO HAY RESPUESTA EN EL SERVICIO TELEFóNICO

TOPIC 4TH: MALA ATENCIóN AL CLIENTE EN LA ATENCIóN TELEFóNICA. TOPIC 7TH: RECLAMO

FRAUDES CON TARJETAS DE RETAIL/PERDIDA DE TARJETAS.




















... y las interacciones sociales

Reclamo Inicial

Opiniones/Comentarios

will be the VCoP members, and arcs will represent interaction between them.

How to link the members and how to measure their interactions to complete the

network is our main concern.

Figure 1: A typical forum structure, in circles are the users who posted.

In this work, will be describe two VCoPs’ network representation according

the following replying schema of members:

1. Creator-oriented Network: When a member create a thread, every

reply will be related to him/her. This network representation is the less

dense network (density is measured in terms of the number of arcs that

the network have).

2. Last Reply-oriented Network: Every reply of a thread will be a re-

sponse of the last post. This network representation has a middle density.

3. All Previous-oriented Network: Every reply of a thread will be a

response to all posts which are already in a specific thread. This network

representation is the most dense network.

In figure 2 the latter three approaches of network representations of the

forum are presented. In figure 2, arcs represents members’ replies and nodes

represent the members who made the posts. In a traditional approach, the

weight of arcs will be a simple counter of how many times a given member

replies to other one.

In order to consider the reply of members according to the community pur-

pose (for any of these configurations), and to filter noisy posts, both concept

based and topic based message reduction is performed.

11

Figure 2: Three different network models to represent a given forum interaction.

3.6. Concept-based & Topic-based Network Filtering

Previous work (Rıos et al., 2009) brings a method to evaluate community

goals accomplishment. In this work we will use this approach to classify the

members’ posts according VCoPs’ goals. These goals are defined as a set of

terms, which are composed by a set of keywords or statements in natural lan-

guage.

The idea is to compare with euclidean distance two members’ posts. If the

distance is over a certain threshold θ, an interaction will be considered between

them. We support the idea that this will help us to avoid irrelevant interactions.

For example, in a VCoP with k goals (or topics), let Pj a post of user j that

is a reply to post of user i (Pi). The distance between them will be calculated

with Equation 7.

dm(Pi, Pj) =

�k gikgjk��

k g2ik

�k g

2jk

(7)

Where gik is the score of topic k in post of user i. It is clear that the distanceexists only if Pj is a reply to Pi. After that, the weight of arc ai,j is calculated

according to equation 8.

ai,j =�

i,jdm(Pi,Pj)≥θ

d(Pi, Pj) (8)

We used this weight in all three configurations previously described (Creator-

oriented, Last Reply-oriented & All Previous-oriented). Afterwards, we applied

HITS (Kleinberg, 1999) to find the key members on the different network con-

figurations.

3.7. Network Construction

Algorithm 3.1 presents the pseudo-code on how a the graph Gc = (N ,A) is

build by using the Creator-oriented network, and algorithm 3.3 presents how it

12







guage.






with Equation 7.

dm(Pi, Pj) =

�k gikgjk��

k g2ik

�k g

2jk

(7)



ai,j =�

i,jdm(Pi,Pj)≥θ

d(Pi, Pj) (8)




figurations.




12







guage.






with Equation 7.

dm(Pi, Pj) =

�k gikgjk��

k g2ik

�k g

2jk

(7)



ai,j =�

i,jdm(Pi,Pj)≥θ

d(Pi, Pj) (8)




figurations.




12


Ejemplo 1Retail

D


Se puede visualizar facilmente el imapcto de los reclamos


5. Conectar y buscar relaciones implicitas en redes inconexas

Retail D


Existe una Relación Implicita


Fusión de Redes InconexasRetail

D


Nueva Red: Formada por la Fusion de dos grupos


6. Medir el impacto de los comentarios mediante el analisis

de estas redes sociales

• Una vez que se obtiene la red es posible:

• Calcular los nodos que más impactan la red global usando varios algoritmos como: HITS o PageRank

• Obtener la densidad de la red

• In-degree y out-degree (grados de separación)

• Entre otros


Conclusión


Conclusión

• Desde Sitios Web Sociales Es posible encontrar información que pude ser usada para mejorar los procesos internos de provisión de bienes, servicios, etc.


Referencias1) “Usuarios chilenos investigan en internet antes de comprar”, Reportaje en Cooperativa.cl,

10th jan. 2010

2) “Reporte soy Digital 2010”, AyerViernes, www.facebook.com/ayerviernes

3) “Social Media”, Veronica Peng

4) “Building Web Reputation Systems”, Google Tech Talk, Randy Farmer, 1st Jul 2010

5) “Chapter 9: Social Web Mining” on Advanced Techniques on Web Intelligence, Sebastián A. Ríos & Felipe Aguilera, Chapter of Book, Springer Verlag, 2010, 284 pp.

6) “Virtual Communities of Practice’s Purpose Evolution Analysis Using a Concept-Based Mining Approach”, Sebastián A. Ríos, Felipe Aguilera and Luis A. Guerrero, Knowledge-Based Intelligent Information and Engineering Systems - Part II; Lecture Notes in Computer Science, 2009 vol. 5712 pp. 480-489


http://www.facebook.com/ayerviernes

http://www.facebook.com/ayerviernes

http://www.springerlink.com/content/?Author=Sebasti%c3%a1n+A.+R%c3%ados

http://www.springerlink.com/content/?Author=Sebasti%c3%a1n+A.+R%c3%ados

http://www.springerlink.com/content/?Author=Felipe+Aguilera

http://www.springerlink.com/content/?Author=Felipe+Aguilera

http://www.springerlink.com/content/?Author=Luis+A.+Guerrero

http://www.springerlink.com/content/?Author=Luis+A.+Guerrero

Documents

Descubriendo información comercial desde la web social.€¦ · Descubriendo información comercial desde la web social. Sebastián A. Ríos Departamento de Ing. Industrial ... personas