Extract and Analyze Key Terms from Research Groups

Identifies and extracts key terms from titles and abstracts of publications within different research groups using natural language processing techniques, and computes term statistics including TF-IDF scores.

Usage

sniff_groups_terms(
  net_groups,
  algorithm = "rake",
  phrase_pattern = "(A|N)*N(P+D*(A|N)*N)*",
  n_cores = 1,
  show_progress = TRUE,
  n_terms = 15,
  min_freq = 2,
  digits = 4
)

Arguments

net_groups

A list containing network data with publication information. Must include elements: network (with vertex attributes 'group', 'TI', 'AB'), pubs_by_year, and aggregate.

algorithm

Term extraction algorithm to use. Options are:

• "rake" - Rapid Automatic Keyword Extraction (default)

• "pointwise" - Pointwise Mutual Information

• "phrase" - Phrase pattern matching

phrase_pattern

Regular expression pattern for phrase extraction when algorithm = "phrase" (default: "(A|N)N(P+D(A|N)N)")

n_cores

Number of CPU cores to use for parallel processing (default: 1)

show_progress

Logical indicating whether to show progress bar (default: TRUE)

n_terms

Number of top terms to return in summary table (default: 15)

min_freq

Minimum frequency threshold for terms (default: 2)

digits

Number of decimal places to round numerical values (default: 4)

Value

A list with two components:

• terms_by_group: A named list (by group) of data frames containing extracted terms with statistics

• terms_table: A summary tibble with top terms by frequency and TF-IDF for each group

Details

This function performs the following steps:

1. Validates input structure and parameters

2. Loads the UDPipe language model from package extdata

3. Processes text data (titles and abstracts) for each group

4. Applies the selected term extraction algorithm (RAKE, PMI, or phrase patterns)

5. Computes term frequencies and TF-IDF scores

6. Returns ranked terms for each research group with comprehensive statistics

The function uses UDPipe for tokenization, lemmatization and POS tagging before term extraction. For phrase extraction, the default pattern finds noun phrases.

Examples

if (FALSE) { # \dontrun{
# Assuming groups is output from sniff_groups()
terms <- sniff_groups_terms(groups, algorithm = "rake")

# View terms for first group
head(terms$terms_by_group[[1]])

# View summary table
print(terms$terms_table)

# Customized extraction
net_groups_terms <- sniff_groups_terms(net_groups,
  algorithm = "phrase",
  n_terms = 10,
  min_freq = 3,
  n_cores = 4
)
} # }