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wordorientation

Detect attraction and repulsion between words in text.

What it does

For every pair of words that co-occur often enough to analyze, wordorientation computes the phi coefficient (a correlation measure for binary co-occurrence data), tests it for significance, and classifies the pair as:

• attraction — the words co-occur significantly more than chance would predict
• repulsion — the words co-occur significantly less than chance would predict
• neutral — no significant relationship at the chosen threshold

Installation

# once on CRAN
install.packages("wordorientation")

# development version
# devtools::install_github("yourusername/wordorientation")

Usage

library(wordorientation)

result <- analyze_word_orientation(
  example_social_posts(),
  text_col = "text", doc_col = "id",
  min_count = 2
)

head(result$scored)
plot_orientation_network(result$scored)

Or step by step:

tokens <- tokenize_posts(my_data, text_col = "text")
cooc   <- cooccurrence_counts(tokens, min_count = 5)
scored <- word_orientation(cooc, alpha = 0.05)

How it differs from related packages

• widyr::pairwise_cor() computes the same underlying phi-style correlation but is a general tidy-correlation tool, not text-specific: it has no built-in tokenization, no significance-based classification, and no network plotting.
• collostructions measures the attraction/repulsion of words to grammatical constructions, not to each other.
• MadanTextNetwork provides a co-occurrence network Shiny app but is built specifically for Persian text and does not classify pairs by statistical significance.

wordorientation combines tokenization, co-occurrence counting, significance-tested classification, and network visualization into a single general-language pipeline.

Multiple comparisons

By default, word_orientation() applies a Benjamini-Hochberg correction across all tested word pairs (p_adjust_method = "BH"). On small corpora with few documents, this can mean no pair survives correction even when raw phi values look large — this is intentional, not a bug: it guards against over-interpreting spurious associations from sparse data.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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