How do you measure word frequency in your corpus analysis?

One of the most important metrics for language use is word frequency, which measures how often a term appears in a given text or corpus. In contrast to absolute frequency, which only provides raw numbers without any context, relative frequency takes into account the overall number of words and provides a proportion. If the word "dog" appears 10 times in a text of 100 words, its relative frequency is 10%, indicating that it is highly featured. Adjusting for text length or other textual properties like genre enables fair comparisons across different corpora, which in turn reveals linguistic patterns and efficiently informs linguistic research, lexicography, and language instruction.

Word frequency has a significant impact on language acquisition and content analysis. High-frequency words facilitate comprehension and signify the difficulty of a text, facilitating the customization of reading materials for various learning levels. In contrast, texts with low-frequency terms are typically complex and thematic, as well as field-specific. In linguistic research, word frequency aids in the identification of keywords that reflect a text's primary themes, which is essential for summarizing, indexing, and information retrieval tasks. In addition, it guides curriculum development by emphasizing learner-essential vocabulary, thereby expediting educational processes and enhancing communication effectiveness.

Calculating word frequency involves generating a frequency list via corpus analysis tools like AntConc or WordSmith, showcasing each word's occurrence in a text. This analysis illuminates high-frequency words, common in the language, and low-frequency ones, often more topic-specific. Techniques include dividing words into frequency bands, grouping them based on occurrence similarity, or using measures like type/token ratio for vocabulary diversity. Such methods aid in linguistic research, readability assessments, and educational material preparation, requiring various software depending on the complexity of the task.

Thank you for this. I did not know of this formal method but in my MA work on virtues and character strengths among physicians I counted the word describing them from short narratives of a "role model" doctor that the participants had known. The words (such as "good willed" or "knowledgeable") were then ranked accordingly and certain clusters of related strengths could be formed. For example, clusters of virtues of teamwork, kindness, knowledge and a few more. I didn't use frequency but virtues in in high numbers obviously wee most frequent. This method allowed for non-binding (open), in contrast to given choices from a list, contribution of participants of what character strengths they valued. Is there something helpful to add to this?

During my master's degree at the Università per Stranieri di Perugia (Unistrapg), I delved into corpus analysis, focusing on word frequency within the Italian language. This experience underscored the dynamic nature of language use across different contexts. For instance, a word common in everyday conversation could be considered rare in literary or formal texts. This variability poses intriguing challenges, particularly in translation or language instruction, necessitating a nuanced approach to corpus linguistics. Beyond academia, these principles are crucial in digital realms like SEO, where understanding keyword frequency directly impacts online engagement.