Ingeniería de mercados - clase 21
- Andrés Esteban Acero, PhD (c) (andres.acerol@usa.edu.co)
Profesor del programa de Ingeniería Industrial, Universidad Sergio Arboleda. Candidato a doctor en Ingeniería de la Universidad de los Andes.
Minería de textos
En esta clase consideramos el problema de analizar datos de texto. El texto es quizás la forma de datos más ubicua. La cantidad de datos de texto disponibles es asombrosa: desde libros o artículos hasta páginas web y redes sociales. Todos ellos forman una rica fuente de datos que podemos explotar para obtener información valiosa. El análisis de datos de texto tiene muchas aplicaciones prácticas, incluida la recuperación de información, el análisis de redes sociales, el filtrado de correo no deseado y el análisis de sentimientos.
La minería de texto, o análisis de texto como se le conoce alternativamente, es la tarea de extraer información de los datos de texto. En las clases anteriores, analizamos el uso de R para realizar análisis de datos en varios entornos. Había un elemento común en todos los entornos: los datos de entrada que usamos casi siempre estaban estructurados, es decir, consistían en puntos de datos para un conjunto dado de variables, ya fueran numéricas o categóricas. Los datos de texto son desestructurados. Aunque el texto escrito en cualquier lenguaje humano se rige por las reglas de la gramática, carece de las variables claramente definidas que tenemos al analizar datos estructurados. Además de las reglas lingüísticas, el texto contiene información, como describir un objeto o presentar un punto de vista. A su vez, el objetivo de la minería de texto es extraer esta información latente de los datos de texto de la manera más objetiva posible.
La minería de texto es un término general para los diferentes tipos de análisis que realizamos sobre el texto. Las tareas de análisis van desde un simple análisis basado en la frecuencia de palabras hasta tareas más complejas como la clasificación. De hecho, podemos adaptar fácilmente las técnicas clásicas de análisis de datos, como el análisis y la clasificación de datos exploratorios, cuando trabajas con datos de texto. Solo necesitamos usar la representación adecuada del texto para que los datos de texto no estructurados se puedan codificar como datos estructurados o semiestructurados.
El día de hoy vamos a trabajar con datos de twitter. Twitter es actualmente una dinámica e ingente fuente de contenidos que, dada su popularidad e impacto, se ha convertido en la principal fuente de información para estudios de Social Media Analytics. Análisis de reputación de empresas, productos o personalidades, estudios de impacto relacionados con marketing, extracción de opiniones y predicción de tendencias son sólo algunos ejemplos de aplicaciones. Hoy trabajaremos con una muestra de los tweets de tres personalidades: Elon Musk, Bill Gates y el alcalde de San Francisco, Ed Lee.
data <- read.csv("tweets.csv", sep=";")
table(data$screen_name)##
## BillGates elonmusk mayoredlee
## 53 48 40colnames(data)## [1] "screen_name" "user_id"
## [3] "created_at" "status_id"
## [5] "text" "retweet_count"
## [7] "favorite_count" "is_quote_status"
## [9] "quote_status_id" "is_retweet"
## [11] "retweet_status_id" "in_reply_to_status_status_id"
## [13] "in_reply_to_status_user_id" "in_reply_to_status_screen_name"
## [15] "lang" "source"
## [17] "media_id" "media_url"
## [19] "media_url_expanded" "urls"
## [21] "urls_display" "urls_expanded"
## [23] "mentions_screen_name" "mentions_user_id"
## [25] "symbols" "hashtags"
## [27] "coordinates" "place_id"
## [29] "place_type" "place_name"
## [31] "place_full_name" "country_code"
## [33] "country" "bounding_box_coordinates"
## [35] "bounding_box_type"De entre toda la información disponible, en esta clase solamente trabajaremos con: autor del tweet, fecha de publicación, identificador del tweet y contenido. Para realizar este arreglo de datos y la limpieza de los mismos usaremos el siguiente código:
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.6.3## -- Attaching packages --------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## Warning: package 'ggplot2' was built under R version 3.6.3## Warning: package 'tibble' was built under R version 3.6.3## Warning: package 'tidyr' was built under R version 3.6.3## Warning: package 'readr' was built under R version 3.6.3## Warning: package 'purrr' was built under R version 3.6.3## Warning: package 'dplyr' was built under R version 3.6.3## Warning: package 'stringr' was built under R version 3.6.3## Warning: package 'forcats' was built under R version 3.6.3## -- Conflicts ------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(knitr)## Warning: package 'knitr' was built under R version 3.6.3tweets <- data %>% select(screen_name, created_at, status_id, text)
colnames(tweets) <- c("autor", "fecha", "ID", "texto")
head(tweets)El proceso de limpieza de texto, dentro del ámbito de text mining, consiste en eliminar del texto todo aquello que no aporte información sobre su temática, estructura o contenido. No existe una única forma de hacerlo, depende en gran medida de la finalidad del análisis y de la fuente de la que proceda el texto. Por ejemplo, en las redes sociales los usuarios pueden escribir de la forma que quieran, lo que suele resultar en un uso elevado de abreviaturas y signos de puntuación.
limpiar_tokenizar <- function(texto){
# El orden de la limpieza no es arbitrario
# Se convierte todo el texto a minúsculas
nuevo_texto <- tolower(texto)
# Eliminación de páginas web (palabras que empiezan por "http." seguidas
# de cualquier cosa que no sea un espacio)
nuevo_texto <- str_replace_all(nuevo_texto,"http\\S*", "")
# Eliminación de signos de puntuación
nuevo_texto <- str_replace_all(nuevo_texto,"[[:punct:]]", " ")
# Eliminación de números
nuevo_texto <- str_replace_all(nuevo_texto,"[[:digit:]]", " ")
# Eliminación de espacios en blanco múltiples
nuevo_texto <- str_replace_all(nuevo_texto,"[\\s]+", " ")
# Tokenización por palabras individuales
nuevo_texto <- str_split(nuevo_texto, " ")[[1]]
# Eliminación de tokens con una longitud < 2
nuevo_texto <- keep(.x = nuevo_texto, .p = function(x){str_length(x) > 1})
return(nuevo_texto)
}Puede observarse que la función limpiar_tokenizar() elimina el símbolo @ y # de las palabra a las que acompañan. En Twitter, los usuarios se identifican de esta forma, por lo que @ y # pertenecen al nombre. Aunque es importante tener en cuenta las eliminaciones del proceso de limpieza, el impacto en este caso no es demasiado alto, ya que, si un documento se caracteriza por tener la palabra #datascience, también será detectado fácilmente mediante la palabra datascience.
tweets <- tweets %>% mutate(texto_tokenizado = map(.x = texto, .f = limpiar_tokenizar))
tweets %>% select(texto_tokenizado) %>% head()
tweets_tidy <- tweets %>% select(-texto) %>% unnest()## Warning: `cols` is now required when using unnest().
## Please use `cols = c(texto_tokenizado)`tweets_tidy <- tweets_tidy %>% rename(token = texto_tokenizado)Análisis exploratorio de los datos
Dado que cada usuario puede haber iniciado su actividad en Twitter en diferente momento, es interesante explorar si los tweets recuperados solapan en el tiempo.
library(lubridate)## Warning: package 'lubridate' was built under R version 3.6.3##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionggplot(tweets, aes(x = as.Date(fecha), fill = autor)) + geom_histogram(position = "identity", bins = 20, show.legend = FALSE) + labs(x = "fecha de publicación", y = "número de tweets") + facet_wrap(~ autor, ncol = 1)
Puede observarse un perfil de actividad distinto para cada usuario. Bill Gates ha mantenido una actividad constante de en torno a 5 tweets por mes durante todo el periodo estudiado. Elon Musk muestra una actividad inicial por encima de la de Bill Gates pero esta decae. Ed Lee tiene una actividad muy alta sobre todo entre octubre y noviembre.
Frecuencia de palabras
Total de palabras por usuario
tweets_tidy %>% group_by(autor) %>% summarise(n = n()) ## `summarise()` ungrouping output (override with `.groups` argument)tweets_tidy %>% ggplot(aes(x = autor)) + geom_bar() + coord_flip() + theme_bw() 
Palabras distintas utilizadas por cada usuario
tweets_tidy %>% select(autor, token) %>% distinct() %>% group_by(autor) %>% summarise(palabras_distintas = n()) ## `summarise()` ungrouping output (override with `.groups` argument)tweets_tidy %>% select(autor, token) %>% distinct() %>%
ggplot(aes(x = autor)) + geom_bar() + coord_flip() + theme_bw()
Palabras utilizadas por usuario
tweets_tidy %>% group_by(autor, token) %>% count(token) %>% group_by(autor) %>% top_n(10, n) %>% arrange(autor, desc(n)) %>% print(n=30)## # A tibble: 32 x 3
## # Groups: autor [3]
## autor token n
## <fct> <chr> <int>
## 1 BillGates the 41
## 2 BillGates to 34
## 3 BillGates of 25
## 4 BillGates in 19
## 5 BillGates and 16
## 6 BillGates we 15
## 7 BillGates can 11
## 8 BillGates for 10
## 9 BillGates is 10
## 10 BillGates on 10
## 11 elonmusk the 16
## 12 elonmusk it 15
## 13 elonmusk is 14
## 14 elonmusk to 13
## 15 elonmusk hyperloop 10
## 16 elonmusk for 8
## 17 elonmusk in 7
## 18 elonmusk not 7
## 19 elonmusk spacex 7
## 20 elonmusk and 6
## 21 elonmusk but 6
## 22 elonmusk so 6
## 23 mayoredlee to 33
## 24 mayoredlee sf 25
## 25 mayoredlee the 25
## 26 mayoredlee our 20
## 27 mayoredlee we 18
## 28 mayoredlee amp 17
## 29 mayoredlee of 16
## 30 mayoredlee are 14
## # ... with 2 more rowsStop words
En la tabla anterior puede observarse que los términos más frecuentes en todos los usuarios se corresponden con artículos, preposiciones, pronombres…, en general, palabras que no aportan información relevante sobre el texto. Ha estas palabras se les conoce como stopwords. Para cada idioma existen distintos listados de stopwords, además, dependiendo del contexto, puede ser necesario adaptar el listado. En la tabla anterior aparece el término amp que procede de la etiqueta html &. Con frecuencia, a medida que se realiza un análisis se encuentran palabras que deben incluirse en el listado de stopwords.
lista_stopwords <- c('me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves',
'you','your', 'yours', 'yourself', 'yourselves', 'he', 'him','his',
'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself',
'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which',
'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are',
'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had',
'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and',
'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at',
'by', 'for', 'with', 'about', 'against', 'between', 'into',
'through', 'during', 'before', 'after', 'above', 'below', 'to',
'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under',
'again', 'further', 'then', 'once', 'here', 'there', 'when',
'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',
'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own',
'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will',
'just', 'don', 'should', 'now', 'd', 'll', 'm', 'o', 're', 've',
'y', 'ain', 'aren', 'couldn', 'didn', 'doesn', 'hadn', 'hasn',
'haven', 'isn', 'ma', 'mightn', 'mustn', 'needn', 'shan',
'shouldn', 'wasn', 'weren', 'won', 'wouldn','i')
# Se añade el término amp al listado de stopwords
lista_stopwords <- c(lista_stopwords, "amp")
# Se filtran las stopwords
tweets_tidy <- tweets_tidy %>% filter(!(token %in% lista_stopwords))Representación gráfica de las frecuencias
tweets_tidy %>% group_by(autor, token) %>% count(token) %>% group_by(autor) %>%
top_n(10, n) %>% arrange(autor, desc(n)) %>%
ggplot(aes(x = reorder(token,n), y = n, fill = autor)) +
geom_col() +
theme_bw() +
labs(y = "", x = "") +
theme(legend.position = "none") +
coord_flip() +
facet_wrap(~autor,scales = "free", ncol = 1, drop = TRUE)
Los resultados obtenidos tienen sentido si ponemos en contexto la actividad profesional de los usuarios analizados. Mayor Ed Lee es alcalde de San Francisco (sf), por lo que sus tweets están relacionados con la ciudad, residentes, familias, casas… Elon Musk dirige varias empresas tecnológicas entre las que destacan Tesla y SpaceX, dedicadas a los coches y a la aeronáutica. Por último, Bill Gates, además de propietario de microsoft, dedica parte de su capital a fundaciones de ayuda, de ahí las palabras mundo, estudiantes, ayuda…
Nubes de palabras
library(wordcloud)## Warning: package 'wordcloud' was built under R version 3.6.3## Loading required package: RColorBrewerlibrary(RColorBrewer)
wordcloud_custom <- function(grupo, df){
print(grupo)
wordcloud(words = df$token, freq = df$frecuencia,
max.words = 400, random.order = FALSE, rot.per = 0.35,
colors = brewer.pal(8, "Dark2"))
}
df_grouped <- tweets_tidy %>% group_by(autor, token) %>% count(token) %>%
group_by(autor) %>% mutate(frecuencia = n / n()) %>%
arrange(autor, desc(frecuencia)) %>% nest()
walk2(.x = df_grouped$autor, .y = df_grouped$data, .f = wordcloud_custom)## [1] BillGates
## Levels: BillGates elonmusk mayoredlee## Warning in wordcloud(words = df$token, freq = df$frecuencia, max.words = 400, :
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## [1] elonmusk
## Levels: BillGates elonmusk mayoredlee## Warning in wordcloud(words = df$token, freq = df$frecuencia, max.words = 400, :
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## [1] mayoredlee
## Levels: BillGates elonmusk mayoredlee
¿Quieres ser tan gomoso como el profesor? Los invito a aquellos que quieran hacer proyectos interesantes a unirse a los proyectos de los semilleros SITDIO y eficiencia, eficacia y sostenibilidad. Allá podrás explorar y aprender muchas herramientas de la mano de los profesores.