Short for automatic knowledge classification, akc is an R package used to carry out keyword classification based on network science (mainly community detection techniques), using bibliometric data. However, these provided functions are general, and could be extended to solve other tasks in text mining as well. Main functions are listed as below:
keyword_clean - Automatic keyword cleaning and transfer to tidy formatkeyword_extract - Extract keywords from raw textkeyword_merge - Merge keywords that supposed to have same meaningskeyword_group - Construct network from a tidy table and divide them into groupskeyword_table - Display the table with different groups of keywordskeyword_vis - Visualization of grouped keyword co-occurrence networkGenerally provides a tidy framework of data manipulation supported by dplyr, akc was written in data.table when necessary to guarantee the performance for big data analysis. Meanwhile, akc also utilizes the state-of-the-art text mining functions provided by stringr,tidytext,textstem and network analysis functions provided by igraph,tidygraph and ggraph. Pipe %>% has been exported from magrittr and could be used directly in akc.
Logo of akc package.
# load pakcage
library(akc)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
# inspect the built-in data
bibli_data_table
#> # A tibble: 1,448 x 4
#> id title keyword abstract
#> <int> <chr> <chr> <chr>
#> 1 1 Keeping the doors ope~ Austerity; community c~ "English public librari~
#> 2 2 Comparison of Sloveni~ Comparative librarians~ "This paper aims to pro~
#> 3 3 Analysis of the facto~ Continuation will of v~ "This study aims to dev~
#> 4 4 Redefining Library an~ Curriculum; education ~ "The purpose of this st~
#> 5 5 Can in-house use data~ Check-out use; circula~ "Libraries worldwide ar~
#> 6 6 Practices of communit~ Community councillors;~ "The purpose of the res~
#> 7 7 Exploring Becoming, D~ Library and Informatio~ "Professional identity ~
#> 8 8 Predictors of burnout~ Emotional exhaustion; ~ "Work stress and profes~
#> 9 9 The Roma and document~ Academic libraries; co~ "This paper explores th~
#> 10 10 Mediation effect of k~ Job performance; knowl~ "This paper proposes a ~
#> # ... with 1,438 more rowsThe data set contains bibliometric data on topic of “academic library”,it is a data.frame of 4 columns(with docuent ID,article title,keyword and abstract), more information could be found via ?bibli_data_data.If the user want to carry out tasks by simply copying the example codes,make sure to arrange the data in the same format as biblio_data_table and set the same names for the corresponding columns.
The entire cleaning processes include: 1.Split the text with separators; 2.Reomve the contents in the parentheses (including the parentheses); 3.Remove whitespaces from start and end of string and reduces repeated whitespaces inside a string; 4.Remove all the null character string and pure number sequences; 5.Convert all letters to lower case; 6.Lemmatization (not in default setting because it is not recommended unless you need a relatively rough result. For better merging, use keyword_merge displayed below).
bibli_data_table %>%
keyword_clean() -> clean_data
clean_data
#> # A tibble: 5,378 x 2
#> id keyword
#> <int> <chr>
#> 1 1 austerity
#> 2 1 community capacity
#> 3 1 library professional
#> 4 1 public libraries
#> 5 1 public service delivery
#> 6 1 volunteer relationship management
#> 7 1 volunteering
#> 8 2 comparative librarianship
#> 9 2 korea
#> 10 2 library legislation
#> # ... with 5,368 more rowsMerge keywords that have common stem or lemma, and return the majority form of the word.
clean_data %>%
keyword_merge() -> merged_data
merged_data
#> # A tibble: 5,372 x 2
#> id keyword
#> <int> <chr>
#> 1 1163 10.7202/1063788ar
#> 2 619 18th century
#> 3 1154 1password
#> 4 81 1science
#> 5 361 second-career librarianship
#> 6 662 second life
#> 7 1424 2016 us presidential election
#> 8 42 21st-century skills
#> 9 1114 21st century skills
#> 10 1051 24-hour opening
#> # ... with 5,362 more rowsCreate a tbl_graph(a class provided by tidygraph package) from the tidy table with document ID and keyword. Each entry(row) should contain only one keyword in the tidy format.
merged_data %>%
keyword_group() -> grouped_data
grouped_data
#> # A tbl_graph: 207 nodes and 1332 edges
#> #
#> # An undirected simple graph with 1 component
#> #
#> # Node Data: 207 x 3 (active)
#> name freq group
#> <chr> <int> <int>
#> 1 academic librarians 7 1
#> 2 academic libraries 145 1
#> 3 acquisitions 12 3
#> 4 africa 6 1
#> 5 altmetrics 7 3
#> 6 artificial intelligence 4 1
#> # ... with 201 more rows
#> #
#> # Edge Data: 1,332 x 3
#> from to n
#> <int> <int> <dbl>
#> 1 1 2 4
#> 2 2 3 2
#> 3 2 48 1
#> # ... with 1,329 more rowsThe output table would show the top 10 keywords (by occurrence) and their frequency. Keywords are separated by “;”.
grouped_data %>%
keyword_table(top = 10)
#> # A tibble: 5 x 2
#> Group `Keywords(TOP 10)`
#> <int> <chr>
#> 1 1 academic libraries (145); information literacy (58); university librari~
#> 2 2 public libraries (74); libraries (65); digital libraries (31); library ~
#> 3 3 open access (32); bibliometrics (31); library and information science (~
#> 4 4 leadership (12); library management (12); research data management (12)~
#> 5 5 national libraries (10); culture (7); cataloguing (6); knowledge organi~Keyword co-occurrence network in different groups. Colors are used to specify the groups, the size of nodes is proportional to the keyword frequency, while the alpha of edges is proportional to the co-occurrence relationship between keywords.
To extract keywords from the abstract using the keywords as a dictionary. More pre-processing filter should be implemented afterward, such as cleaning, keyword merging and filtering by term frequency or tf-idf. It is suggested to keep the size down before using keyword_group.
bibli_data_table %>%
keyword_clean(id = "id",keyword = "keyword") %>%
pull(keyword) %>%
make_dict -> my_dict
bibli_data_table %>%
keyword_extract(id = "id",text = "abstract",dict = my_dict) %>%
keyword_merge(keyword = "keyword")
#> # A tibble: 27,130 x 2
#> id keyword
#> <int> <chr>
#> 1 619 18th century
#> 2 1223 18th century
#> 3 1154 1password
#> 4 81 1science
#> 5 983 1science
#> 6 15 2016 us presidential election
#> 7 662 3d environment
#> 8 910 fourth museum assembly
#> 9 624 55th library week
#> 10 747 aasl standards
#> # ... with 27,120 more rows