validmind.Punctuations

Metrics functions for any Pandas-compatible datasets

Punctuations

@tags('nlp', 'text_data', 'visualization', 'frequency_analysis')

@tasks('text_classification', 'text_summarization', 'nlp')

defPunctuations(dataset,count_mode='token'):

Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset.

Purpose

The Punctuations Metric's primary purpose is to analyze the frequency of punctuation usage within a given text dataset. This is often used in Natural Language Processing tasks, such as text classification and text summarization.

Test Mechanism

The test begins by verifying that the input "dataset" is of the type VMDataset. The count_mode parameter must be either "token" (counts punctuation marks as individual tokens) or "word" (counts punctuation marks within words). Following that, a corpus is created from the dataset by splitting its text on spaces. Each unique punctuation character in the text corpus is then tallied. The frequency distribution of each punctuation symbol is visualized as a bar graph, with these results being stored as Figures and associated with the main Punctuations object.

Signs of High Risk

  • Excessive or unusual frequency of specific punctuation marks, potentially denoting dubious quality, data corruption, or skewed data.

Strengths

  • Provides valuable insights into the distribution of punctuation usage in a text dataset.
  • Important in validating the quality, consistency, and nature of the data.
  • Can provide hints about the style or tonality of the text corpus, such as informal and emotional context indicated by frequent exclamation marks.

Limitations

  • Focuses solely on punctuation usage, potentially missing other important textual characteristics.
  • General cultural or tonality assumptions based on punctuation distribution can be misguiding, as these vary across different languages and contexts.
  • Less effective with languages that use non-standard or different punctuation.
  • Visualization may lack interpretability when there are many unique punctuation marks in the dataset.