Example from Programming4Humanists

100%

Example from Programming4Humanists

✓ Published0🌍 Public

Last edited Nov 6, 2015

Created on Nov 6, 2015

This example visualizes word frequency in a text by processing the poem’s lines and filtering out common stopwords. It shows a ranked list of words by their occurrence count, displayed in descending order. The code uses XQuery 3.0 with functions like `fn:tokenize`, `fn:distinct-values`, and `fn:count` to compute frequencies, while reading the source text and stopword list from gist URLs. The rendering approach is a simple textual output of word elements, each carrying a `frequency` attribute, rather than a graphical chart.

AI-generated description

Similar vizzes

Loading thumbnail…

For XQuery4Humanists

This example visualizes the frequency and distribution of words extracted from a TEI-encoded poem, processing the text to remove punctuation, normalize case, and tokenize it into a list. The visualization shows the resulting word list as a simple textual display, highlighting the raw output of the XQuery processing pipeline. The code uses XQuery 3.0 functions like `fn:tokenize`, `fn:lower-case`, and `fn:replace` to clean the data, pulling the source directly from a remote XML file hosted on GitHub. The rendering relies solely on the XQuery return, with no additional JavaScript or charting libraries involved.

CCliffordAnderson

79% match

Loading thumbnail…

Produces word frequences for any given sequence of text nodes

This visualization shows word frequencies extracted from a sequence of text nodes, displaying each unique word with its frequency count. The XQuery code collects text from the TEI-encoded source document, normalizes tokens by removing punctuation and lowercasing, then counts occurrences of each distinct word. It returns an ordered dictionary element with word-frequency pairs sorted in descending order. The example uses the XQuery 3.0 specification, including functions like `fn:tokenize`, `fn:distinct-values`, and `fn:count`, to process the remote XML data.

CCliffordAnderson

77% match

Loading thumbnail…

This set of functions produces word frequencies for any given sequence of text nodes.

This example computes word frequencies from a sequence of text nodes and returns them as an ordered list of word elements. It first shows the frequency-ranked vocabulary by normalizing the input—converting to lowercase, stripping punctuation, and tokenizing—before counting occurrences of each distinct token. The code uses XQuery 3.0 with functions like `fn:tokenize`, `fn:distinct-values`, and `fn:count` to group and sort the results. The final output is an XML `<words>` element containing individual `<word>` elements with a `frequency` attribute, ordered from most to least frequent. The data source is the `//tei:l` elements from a TEI-encoded text.

CCliffordAnderson

77% match

Loading thumbnail…

Gist 2a036239a620a1b1a6ee

This example demonstrates a stopword filtering visualization. It shows a collection of common English words—"the," "a," "in," "of," and "as"—rendered as a simple text list, visually distinguishing them from a larger text corpus. The implementation uses an XML-based data structure to define the stopword set, which is then parsed and displayed. The code relies on standard browser APIs for XML parsing and DOM manipulation, presenting the filtered words in a clean, readable format. The visualization makes no attempt at complex layout or animation, instead offering a straightforward, static view of which terms are excluded from analysis, with the underlying data sourced directly from the embedded XML file.

CCliffordAnderson

71% match

Loading thumbnail…

Produces word frequency list for speakers in Much Ado about Nothing.

This example uses XQuery 3.0 with the TEI namespace to generate a per-speaker word frequency list from Shakespeare’s *Much Ado about Nothing*. It processes the TEI-encoded XML document, iterating over distinct speakers and counting each word’s occurrences within their spoken lines, then outputs a sequence of `<word>` elements with the speaker, type, count, and weighted frequency. The query relies on `fn:distinct-values`, `fn:count`, and an `order by` clause to rank terms.

CCliffordAnderson

68% match

Loading thumbnail…

BaseX Stopword List

This visualization uses the BaseX stopword list as its dataset, showing the frequency and distribution of common English words in a compact, typographic layout. It displays each stopword as a text label, with visual weight reflecting word length and prominence. The rendering employs D3.js’s `d3.forceSimulation` to arrange the words in a non-overlapping, space-filling cluster, while a custom `d3.scaleLinear` maps word frequency to font size, creating a tag-cloud effect. The source data comes directly from the plain-text `stopwords.txt` file, parsed client-side, and the layout uses SVG text elements with CSS transitions for smooth positioning updates. The visualization highlights the repetitive structure of natural language processing filters.

CCliffordAnderson

68% match

Loading thumbnail…

Knuth/McIlroy word count task using Haskell

This example demonstrates a word-frequency analysis task using Haskell, showing how to compute and display the most common words from standard input. It chains data transformations through a pipeline: converting text to lowercase, replacing non-letter characters with newlines, splitting into words, and tallying counts with a hash map. The code relies on `Data.HashMap.Strict` for efficient key-value aggregation via `HashMap.fromListWith`, and uses `Data.List` for sorting and taking the top *n* results. The final output lists each word with its count, sorted in descending order of frequency. The example highlights functional composition and lazy evaluation in a concise, read-backwards data-flow style.

FFranklin Chen

67% match

Loading thumbnail…

Gist 2116a7bda7ef9a6364f3

This example demonstrates how to use NLTK’s frequency distribution object to analyze word frequencies in the pre‑loaded text of Monty Python and the Holy Grail. It first displays the top 50 most frequent words, then guides the viewer through tasks that involve examining the raw text, modifying it, and rerunning the frequency calculation. A cumulative frequency plot is generated, and a list comprehension isolates words longer than five characters that occur at least five times, sorted alphabetically. The code relies on NLTK’s `FreqDist` and the `nltk.book` collection.

MMartiHearst

65% match