Teoría

La mineria de texto (TM) es el proceso de extraer información útil, patrones o conocimiento de texto no estructurados.

Consta de 3 etapas: 1. Obtener datos: El reconocimiento óptico de caracteres (OCR) es una tecnología que permite convertir imágenes de texto en texto editable. También es conocido como extracción de texto de imagenes.

  1. Explorar datos: Representación gráfica o visual de los datos para su interpretación. Los métodos más comunes son el Análisis de sentimientos, La nube de palabras y el Topic Modeling.

  2. Análisis predictivo: Son las técnicas y modelos estadísticos para predecir resultados futuros. Los modelos más usados son el Random Forest, Redes Neuronales y Regresiones.

Instalar librerias y paquetes

# install.packages("tidyverse") #Data wrangling
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.0     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# install.packages("tesseract") #OCR
library(tesseract)

# install.packages("magick") #PNG
library(magick)
## Linking to ImageMagick 6.9.12.98
## Enabled features: cairo, freetype, fftw, ghostscript, heic, lcms, pango, raw, rsvg, webp
## Disabled features: fontconfig, x11
# install.packages("officer") #Office Word
library(officer)

# install.packages("pdftools") #PDF 
library(pdftools)
## Using poppler version 23.08.0
# install.packages("purrr") #Para la función map para aplicar una función a cada elemento de un vector 
library(purrr)

# install.packages("tm") #Text Mining
library(tm)
## Loading required package: NLP
## 
## Attaching package: 'NLP'
## 
## The following object is masked from 'package:ggplot2':
## 
##     annotate
# install.packages("RColorBrewer") #Colores
library(RColorBrewer)

# install.packages("wordcloud") #Nube de palabras
library(wordcloud)

# install.packages("topicmodels") #Modelos de temas
library(topicmodels)

#install.packages("ggplot2")
library(ggplot2)

library(wordcloud)
library(RColorBrewer)
library(officer)
library(magrittr)
## 
## Attaching package: 'magrittr'
## 
## The following object is masked from 'package:purrr':
## 
##     set_names
## 
## The following object is masked from 'package:tidyr':
## 
##     extract
library(pdftools)

library(tesseract)
library(tm)
library(magick)

1. Obtener datos mediante OCR

imagen1 <- image_read("C:\\Users\\rodri\\Desktop\\modulo 2\\text minning\\imagen1.PNG") 
texto1 <- ocr(imagen1)
texto1
## [1] "Linear regression with one variable x is also known as univariate linear regression\nor simple linear regression. Simple linear regression is used to predict a single\noutput from a single input. This is an example of supervised learning, which means\nthat the data is labeled, i.e., the output values are known in the training data. Let us\nfit a line through the data using simple linear regression as shown in Fig. 4.1.\n"
doc1 <- read_docx() #Crea un documento en blanco
doc1 <- doc1 %>% body_add_par(texto1, style = "Normal") #Pega texto en el word 
print(doc1, target = "texto1.docx") #Guarda el word en la computadora

Imagen en español PNG a texto en WORD

[Consultar idiomas disponibles] (https://tesseract-ocr.github.io/tessdoc/Data-Files-in-different-versions.html)

imagen2 <- image_read("C:\\Users\\rodri\\Desktop\\modulo 2\\text minning\\imagen2.PNG")
tesseract_download("spa")
## [1] "C:\\Users\\rodri\\AppData\\Local\\tesseract5\\tesseract5\\tessdata/spa.traineddata"
texto2 <- ocr(imagen2, engine = tesseract("spa"))
texto2
## [1] "Un importante, y quizá controversial, asunto político es el que se refiere al efecto del salario mínimo sobre\nlas tasas de desempleo en diversos grupos de trabajadores. Aunque este problema puede ser estudiado con\ndiversos tipos de datos (corte transversal, series de tiempo o datos de panel), suelen usarse las series de\ntiempo para observar los efectos agregados. En la tabla 1.3 se presenta un ejemplo de una base de datos\nde series de tiempo sobre tasas de desempleo y salarios mínimos.\n"
doc2 <- read_docx() #Crea un documento de word en blanco
doc2 <- doc2 %>% body_add_par(texto2, style ="Normal") #Pega el texto en el word
#print(doc2, target = "texto2.docx") #Guarda el word en la computadora

De PDF a texto en Word

#pdf1 <- pdf_convert("C:\\Users\\rodri\\Desktop\\modulo 2\\text minning\\pdf1.pdf", dpi=600) %>% map(ocr)

Actividad 1. Novela “IT”

# Convier el PDF a imágenes
#pdf_convert("C:\\Users\\rodri\\Desktop\\modulo 2\\text minning\\eso3.pdf", dpi = 300, format = "png")
# Crear un  documento de Word
doc <- read_docx()

imagenes <- list.files(pattern = "eso3_.*\\.png$", full.names = TRUE, ignore.case = TRUE)

imagenes <- sort(imagenes)

# Añadir imagenes a un mismo documento
for(imagen in imagenes) {
  doc <- doc %>%
    body_add_img(src = imagen, width = 6, height = 7) %>%  
    body_add_par("") 
}

# Generar y guardar el documento generado

#print(doc, target = "DocumentoConImagenes.docx")

Exploración de datos

Análisis de frecuencias

text <- readLines("http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt") #traer texto de internet

corpus <- Corpus(VectorSource(text)) #Pone renglon en cada celda de vector 
# inspect (corpus)
corpus <- tm_map(corpus, content_transformer(tolower)) #Pone todo en miniscula
## Warning in tm_map.SimpleCorpus(corpus, content_transformer(tolower)):
## transformation drops documents
corpus <- tm_map(corpus, removePunctuation) #Elimina puntuación 
## Warning in tm_map.SimpleCorpus(corpus, removePunctuation): transformation drops
## documents
corpus <- tm_map(corpus, removeNumbers) #Elimina números
## Warning in tm_map.SimpleCorpus(corpus, removeNumbers): transformation drops
## documents
corpus <- tm_map(corpus, removeWords, stopwords ("en")) #Elimina palabras que no hablan del tema
## Warning in tm_map.SimpleCorpus(corpus, removeWords, stopwords("en")):
## transformation drops documents
# corpus <- tm_map(corpus, removeWord, c("dream", "will")) #Elimina palabras puntuales

tdm <- TermDocumentMatrix(corpus) 
m <- as.matrix(tdm) #Cuenta las veces que aparece cada palabra por renglon

frecuencia <- sort(rowSums(m), decreasing = TRUE) #Cuenta la frecuencia de cada palabra en el texto completo






frecuencia_df <- data.frame(word=names(frecuencia),
freq=frecuencia) #Convierte la frecuencia a data frame

ggplot(head(frecuencia_df,10), aes(x=reorder (word, -freq), y=freq)) +
       geom_bar(stat="identity", fill ="lightgreen") + 
       labs (title= "TOP 10 palabras más frecuentes", 
            subtitule= "Discurso 'I have a dream'de M. L. King",  x = "Palabra", y="Frecuencia") + 
  ylim(0,20)

Nube de palabras

#El procesamiento de datos andes de la nube de palabras es igual que en analisis de frecuencias, desde importar el texto hasta frecuencia_df
set.seed(123)
wordcloud(words=frecuencia_df$word,freq = frecuencia_df$freq, min.freq=1,
random.order=FALSE, colors = brewer.pal(8, "RdPu"))

Ejercicio 2. Novela IT

imagenes <- c("eso3_1.png", "eso3_2.png", "eso3_3.png") 
textos <- lapply(imagenes, function(imagen) ocr(image_read(imagen)))
texto_completo <- paste(unlist(textos), collapse = " ")


# Crear un corpus 
corpus <- Corpus(VectorSource(texto_completo))
corpus <- tm_map(corpus, content_transformer(tolower))
## Warning in tm_map.SimpleCorpus(corpus, content_transformer(tolower)):
## transformation drops documents
corpus <- tm_map(corpus, removePunctuation)
## Warning in tm_map.SimpleCorpus(corpus, removePunctuation): transformation drops
## documents
corpus <- tm_map(corpus, removeNumbers)
## Warning in tm_map.SimpleCorpus(corpus, removeNumbers): transformation drops
## documents
corpus <- tm_map(corpus, removeWords, stopwords("en"))
## Warning in tm_map.SimpleCorpus(corpus, removeWords, stopwords("en")):
## transformation drops documents
# Contar la frecuencia
tdm <- TermDocumentMatrix(corpus)
m <- as.matrix(tdm)
frecuencia <- sort(rowSums(m), decreasing = TRUE)

frecuencia_df <- data.frame(word = names(frecuencia), freq = frecuencia)

top_10_palabras <- head(frecuencia_df, 10)

# Ranking de palabras
print(top_10_palabras)
##              word freq
## que           que   33
## george     george   26
## del           del   17
## hacia       hacia   14
## como         como   13
## payaso     payaso   13
## por           por   13
## los           los   11
## una           una   11
## tormenta tormenta   10
View(top_10_palabras)

Ejercicio 2. Novela IT. Nube de palabras

set.seed(123)
wordcloud(words = frecuencia_df$word, freq = frecuencia_df$freq, min.freq = 1,
          random.order = FALSE, colors = brewer.pal(8, "Dark2"))
## Warning in wordcloud(words = frecuencia_df$word, freq = frecuencia_df$freq, :
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---
title: "Text Mining"
author: "Gerardo Cedillo Corona A01704232, Daniel Najera A01709578, Alejandra Suarez A00835247, Eduardo Camacho A01026437"
date: "2024-02-26"
output: 
  html_document: 
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: cosmo
---
![](C:\\Users\\rodri\\Desktop\\modulo 2\\3RhEtfs3tSXAZvgpFEkY945URxe.jpg)

# <span style="color:red;">Teoría</span>
La **mineria de texto** (TM) es el proceso de extraer información útil, patrones
o conocimiento de texto no estructurados.

Consta de 3 etapas: 
1. Obtener datos: El reconocimiento óptico de caracteres (OCR) es una tecnología
que permite convertir imágenes de texto en texto editable. También es conocido 
como **extracción de texto de imagenes**.

2. Explorar datos: Representación gráfica o visual de los datos para su 
interpretación. Los métodos más comunes son el Análisis de sentimientos, 
La nube de palabras y el Topic Modeling.

3. Análisis predictivo: Son las técnicas y modelos estadísticos para predecir 
resultados futuros. Los modelos más usados son el Random Forest, Redes Neuronales
y Regresiones. 

# Instalar librerias y paquetes 

```{r}
# install.packages("tidyverse") #Data wrangling
library(tidyverse)

# install.packages("tesseract") #OCR
library(tesseract)

# install.packages("magick") #PNG
library(magick)

# install.packages("officer") #Office Word
library(officer)

# install.packages("pdftools") #PDF 
library(pdftools)

# install.packages("purrr") #Para la función map para aplicar una función a cada elemento de un vector 
library(purrr)

# install.packages("tm") #Text Mining
library(tm)

# install.packages("RColorBrewer") #Colores
library(RColorBrewer)

# install.packages("wordcloud") #Nube de palabras
library(wordcloud)

# install.packages("topicmodels") #Modelos de temas
library(topicmodels)

#install.packages("ggplot2")
library(ggplot2)

library(wordcloud)
library(RColorBrewer)
library(officer)
library(magrittr)
library(pdftools)

library(tesseract)
library(tm)
library(magick)
```

# <span style="color:red;">1. Obtener datos mediante OCR</span>
```{r}
imagen1 <- image_read("C:\\Users\\rodri\\Desktop\\modulo 2\\text minning\\imagen1.PNG") 
texto1 <- ocr(imagen1)
texto1
doc1 <- read_docx() #Crea un documento en blanco
doc1 <- doc1 %>% body_add_par(texto1, style = "Normal") #Pega texto en el word 
print(doc1, target = "texto1.docx") #Guarda el word en la computadora
```

## <span style="color:red;">Imagen en español PNG a texto en WORD</span>
[Consultar idiomas disponibles] (https://tesseract-ocr.github.io/tessdoc/Data-Files-in-different-versions.html)
```{r}
imagen2 <- image_read("C:\\Users\\rodri\\Desktop\\modulo 2\\text minning\\imagen2.PNG")
tesseract_download("spa")
texto2 <- ocr(imagen2, engine = tesseract("spa"))
texto2
doc2 <- read_docx() #Crea un documento de word en blanco
doc2 <- doc2 %>% body_add_par(texto2, style ="Normal") #Pega el texto en el word
#print(doc2, target = "texto2.docx") #Guarda el word en la computadora
```

## <span style="color:red;">De PDF a texto en Word</span>
```{r}
#pdf1 <- pdf_convert("C:\\Users\\rodri\\Desktop\\modulo 2\\text minning\\pdf1.pdf", dpi=600) %>% map(ocr)
```

## <span style="color:red;">Actividad 1. Novela "IT"</span>
```{r}

# Convier el PDF a imágenes
#pdf_convert("C:\\Users\\rodri\\Desktop\\modulo 2\\text minning\\eso3.pdf", dpi = 300, format = "png")

```

```{r}

# Crear un  documento de Word
doc <- read_docx()

imagenes <- list.files(pattern = "eso3_.*\\.png$", full.names = TRUE, ignore.case = TRUE)

imagenes <- sort(imagenes)

# Añadir imagenes a un mismo documento
for(imagen in imagenes) {
  doc <- doc %>%
    body_add_img(src = imagen, width = 6, height = 7) %>%  
    body_add_par("") 
}

# Generar y guardar el documento generado

#print(doc, target = "DocumentoConImagenes.docx")


```



# <span style="color:red;">Exploración de datos</span>

## <span style="color:red;">Análisis de frecuencias</span>
```{r}
text <- readLines("http://www.sthda.com/sthda/RDoc/example-files/martin-luther-king-i-have-a-dream-speech.txt") #traer texto de internet

corpus <- Corpus(VectorSource(text)) #Pone renglon en cada celda de vector 
# inspect (corpus)
corpus <- tm_map(corpus, content_transformer(tolower)) #Pone todo en miniscula
corpus <- tm_map(corpus, removePunctuation) #Elimina puntuación 
corpus <- tm_map(corpus, removeNumbers) #Elimina números
corpus <- tm_map(corpus, removeWords, stopwords ("en")) #Elimina palabras que no hablan del tema
# corpus <- tm_map(corpus, removeWord, c("dream", "will")) #Elimina palabras puntuales

tdm <- TermDocumentMatrix(corpus) 
m <- as.matrix(tdm) #Cuenta las veces que aparece cada palabra por renglon

frecuencia <- sort(rowSums(m), decreasing = TRUE) #Cuenta la frecuencia de cada palabra en el texto completo






frecuencia_df <- data.frame(word=names(frecuencia),
freq=frecuencia) #Convierte la frecuencia a data frame

ggplot(head(frecuencia_df,10), aes(x=reorder (word, -freq), y=freq)) +
       geom_bar(stat="identity", fill ="lightgreen") + 
       labs (title= "TOP 10 palabras más frecuentes", 
            subtitule= "Discurso 'I have a dream'de M. L. King",  x = "Palabra", y="Frecuencia") + 
  ylim(0,20)
  
```

## <span style="color:red;">Nube de palabras</span>
```{r}
#El procesamiento de datos andes de la nube de palabras es igual que en analisis de frecuencias, desde importar el texto hasta frecuencia_df
set.seed(123)
wordcloud(words=frecuencia_df$word,freq = frecuencia_df$freq, min.freq=1,
random.order=FALSE, colors = brewer.pal(8, "RdPu"))
```

## <span style="color:red;">Ejercicio 2. Novela IT</span>
```{r}


imagenes <- c("eso3_1.png", "eso3_2.png", "eso3_3.png") 
textos <- lapply(imagenes, function(imagen) ocr(image_read(imagen)))
texto_completo <- paste(unlist(textos), collapse = " ")


# Crear un corpus 
corpus <- Corpus(VectorSource(texto_completo))
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removeWords, stopwords("en"))

# Contar la frecuencia
tdm <- TermDocumentMatrix(corpus)
m <- as.matrix(tdm)
frecuencia <- sort(rowSums(m), decreasing = TRUE)

frecuencia_df <- data.frame(word = names(frecuencia), freq = frecuencia)

top_10_palabras <- head(frecuencia_df, 10)

# Ranking de palabras
print(top_10_palabras)

View(top_10_palabras)


```

## <span style="color:red;">Ejercicio 2. Novela IT. Nube de palabras</span>
```{r}

set.seed(123)
wordcloud(words = frecuencia_df$word, freq = frecuencia_df$freq, min.freq = 1,
          random.order = FALSE, colors = brewer.pal(8, "Dark2"))

```






