Detect Attraction and Repulsion Between Words in Text // Provides tools to quantify how strongly pairs of words attract or repel each other in a text corpus, based on co-occurrence patterns. For each word pair, the phi coefficient (a correlation measure for binary variables) is computed from a document-term matrix and tested for significance, then classified as showing attraction (co-occurring more than chance would predict), repulsion (co-occurring less than chance would predict), or no significant relationship. A full pipeline is provided from raw text to a labeled network visualization. Unlike general-purpose pairwise correlation tools, 'wordorientation' is built specifically for text: it handles tokenization and stopword removal, applies significance-based classification rather than reporting a raw correlation coefficient alone, and produces a ready-to-plot attraction/ repulsion network.