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TextAnalysisR Vignette

Source: vignettes/TextAnalysisR_Vignette.Rmd
TextAnalysisR_Vignette.Rmd

The TextAnalysisR package provides a supporting workflow for text mining analysis. The TextAnalysisR.app() function allows users to launch and browse a Shiny app. This web app incorporates ‘quanteda’ (text preprocessing), ‘stm’ (structural topic modeling), ‘ggraph’ as well as ‘widyr’ (network analysis). ‘tidytext’ was implemented to tidy non-tidy format objects.

These steps are similar to those demonstrated in the Shiny web app at TextAnalysisR::TextAnalysisR.app().

Installation

The development version can be installed from GitHub:

install.packages("devtools")
devtools::install_github("mshin77/TextAnalysisR")

Launch and Browse the Shiny app

Unite Text Columns 

df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
united_tbl
## # A tibble: 490 × 7
##    united_texts               reference_type author  year title keyword abstract
##    <chr>                      <chr>          <chr>  <dbl> <chr> <chr>   <chr>   
##  1 Dyscalculia and the minic… journal_artic… Block…  1980 Dysc… Arithm… Notes t…
##  2 The effects of computer-a… thesis         Bukat…  1981 The … locus … This st…
##  3 Computer Assisted Instruc… journal_artic… Watki…  1981 Comp… Comput… Results…
##  4 Arc-Ed Curriculum: Applic… journal_artic… Chaff…  1982 Arc-… Comput… The Arc…
##  5 ARC-ED curriculum: the ap… journal_artic… Chaff…  1982 ARC-… Electr… This ar…
##  6 The Effect of the Hand-he… thesis         Golde…  1982 The … NA      The pur…
##  7 A review of some traditio… journal_artic… Neal,…  1982 A re… tradit… Discuss…
##  8 A study of the effectiven… thesis         Engle…  1983 A st… microc… The pur…
##  9 The influence of computer… thesis         Foste…  1983 The … comput… The eff…
## 10 Using Computer Software t… journal_artic… Pomme…  1983 Usin… Comput… The art…
## # ℹ 480 more rows

Preprocess Text Data 

df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
tokens <- TextAnalysisR::preprocess_texts(united_tbl, text_field = "united_texts", verbose = FALSE)
tokens
## Tokens consisting of 490 documents and 6 docvars.
## text1 :
##  [1] "dyscalculia"    "minicalculator" "alp"            "program"       
##  [5] "arithmetic"     "arithmetic"     "remedial"       "teaching"      
##  [9] "education"      "learning"       "disabled"       "children"      
## [ ... and 71 more ]
## 
## text2 :
##  [1] "effects"        "computer"       "assisted"       "instruction"   
##  [5] "mastery"        "multiplication" "facts"          "learning"      
##  [9] "disabled"       "elementary"     "school"         "aged"          
## [ ... and 54 more ]
## 
## text3 :
##  [1] "computer"    "assisted"    "instruction" "learning"    "disabled"   
##  [6] "students"    "computer"    "assisted"    "instruction" "computer"   
## [11] "programs"    "drills"     
## [ ... and 42 more ]
## 
## text4 :
##  [1] "arc"           "ed"            "curriculum"    "applicability"
##  [5] "severely"      "handicapped"   "pupils"        "computer"     
##  [9] "assisted"      "instruction"   "games"         "online"       
## [ ... and 37 more ]
## 
## text5 :
##  [1] "arc"         "ed"          "curriculum"  "application" "video"      
##  [6] "game"        "formats"     "educational" "software"    "electronic" 
## [11] "games"       "curriculum" 
## [ ... and 89 more ]
## 
## text6 :
##  [1] "effect"      "hand"        "held"        "calculator"  "mathematics"
##  [6] "speed"       "accuracy"    "motivation"  "secondary"   "educable"   
## [11] "mentally"    "retarded"   
## [ ... and 223 more ]
## 
## [ reached max_ndoc ... 484 more documents ]

Plot Word Frequency 

df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
tokens <- TextAnalysisR::preprocess_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
word_frequency_plot <- TextAnalysisR::plot_word_frequency(dfm_object, n = 20)
word_frequency_plot

Evaluate Optimal Number of Topics 

df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
tokens <- TextAnalysisR::preprocess_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
TextAnalysisR::evaluate_optimal_topic_number(
  dfm_object = dfm_object,
  topic_range = 5:30,
  max.em.its = 75,
  categorical_var = "reference_type",
  continuous_var = "year",
  height = 600,
  width = 800,
  verbose = FALSE)

Plot Highest Word Probabilities for Each Topic 

df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
tokens <- TextAnalysisR::preprocess_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
TextAnalysisR::plot_word_probabilities(
  dfm_object = dfm_object,
  topic_n = 15,
  max.em.its = 75,
  categorical_var = "reference_type",
  continuous_var = "year",
  top_term_n = 10,
  ncol = 3,
  height = 1200,
  width = 800,
  verbose = FALSE)

Plot Mean Topic Prevalence Across Documents 

df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
tokens <- TextAnalysisR::preprocess_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
TextAnalysisR::plot_mean_topic_prevalence(
  dfm_object = dfm_object,
  topic_n = 15,
  max.em.its = 75,
  categorical_var = "reference_type",
  continuous_var = "year",
  top_term_n = 10,
  top_topic_n = 15,
  height = 500,
  width = 1000,
  verbose = FALSE)

Plot a Word Co-occurrence Network 

df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
tokens <- TextAnalysisR::preprocess_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
TextAnalysisR::plot_word_co_occurrence_network(
  dfm_object,
  co_occur_n = 200,
  height = 900,
  width = 800)

Plot a Word Correlation Network 

df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
tokens <- TextAnalysisR::preprocess_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
TextAnalysisR::plot_word_correlation_network(
  dfm_object,
  co_occur_n = 30,
  corr_n = 0.4,
  height = 900,
  width = 800)
df <- TextAnalysisR::SpecialEduTech
united_tbl <- TextAnalysisR::unite_text_cols(df, listed_vars = c("title", "keyword", "abstract"))
tokens <- TextAnalysisR::preprocess_texts(united_tbl, text_field = "united_texts")
dfm_object <- quanteda::dfm(tokens)
stm_15 <- TextAnalysisR::stm_15
TextAnalysisR::word_frequency_trends(dfm_object,
                                     stm_model = stm_15,
                                     time_variable = "year",
                                     selected_terms = c("calculator", "computer"),
                                     height = 500,
                                     width = 1000)