WordHoard
Description🔗
WordHoard is a Python 3 module that can obtain antonyms, synonyms, hypernyms, hyponyms, homophones and definitions in the English language.
The Oxford Dictionary defines wordhoard as a supply of words or a lexicon. This Python package was spawned from a Stack Overflow bountied question. That question forced me to look into the best practices for obtaining a comprehensive lists of synonyms for a given word. During my research, I developed the repository synonym discovery and aggregation and decided to create WordHoard.
Primary Use Case🔗
Textual analysis is a broad term for various research methodologies used to qualitatively describe, interpret and understand text data. These methodologies are mainly used in academic research to analyze content related to media and communication studies, popular culture, sociology, and philosophy. Textual analysis allows these researchers to quickly obtain relevant insights from unstructured data. All types of information can be gleaned from textual data, especially from social media posts or news articles. Some of this information includes the overall concept of the subtext, symbolism within the text, assumptions being made and potential relative value to a subject (e.g. data science). In some cases it is possible to deduce the relative historical and cultural context of a body of text using analysis techniques coupled with knowledge from different disciplines, like linguistics and semiotics.
Word frequency is the technique used in textual analysis to measure the frequency of a specific word or word grouping within unstructured data. Measuring the number of word occurrences in a corpus allows a researcher to garner interesting insights about the text. A subset of word frequency is the correlation between a given word and that word's relationship to either antonyms and synonyms within the specific corpus being analyzed. Knowing these relationships is critical to improving word frequencies and topic modeling.
WordHoard was designed to assist researchers performing textual analysis to build more comprehensive lists of antonyms, synonyms, hypernyms, hyponyms and homophones.