validmind.CommonWords

CommonWords

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

@tasks('text_classification', 'text_summarization')

defCommonWords(dataset:validmind.vm_models.VMDataset):

Assesses the most frequent non-stopwords in a text column for identifying prevalent language patterns.

Purpose

The CommonWords metric is used to identify and visualize the most prevalent words within a specified text column of a dataset. This provides insights into the prevalent language patterns and vocabulary, especially useful in Natural Language Processing (NLP) tasks such as text classification and text summarization.

Test Mechanism

The test methodology involves splitting the specified text column's entries into words, collating them into a corpus, and then counting the frequency of each word using the Counter. The forty most frequently occurring non-stopwords are then visualized in an interactive bar chart using Plotly, where the x-axis represents the words, and the y-axis indicates their frequency of occurrence.

Signs of High Risk

  • A lack of distinct words within the list, or the most common words being stopwords.
  • Frequent occurrence of irrelevant or inappropriate words could point out a poorly curated or noisy dataset.
  • An error returned due to the absence of a valid Dataset object, indicating high risk as the metric cannot be effectively implemented without it.

Strengths

  • The metric provides clear insights into the language features – specifically word frequency – of unstructured text data.
  • It can reveal prominent vocabulary and language patterns, which prove vital for feature extraction in NLP tasks.
  • The interactive visualization helps in quickly capturing the patterns and understanding the data intuitively.

Limitations

  • The test disregards semantic or context-related information as it solely focuses on word frequency.
  • It intentionally ignores stopwords, which might carry necessary significance in certain scenarios.
  • The applicability is limited to English-language text data as English stopwords are used for filtering, hence cannot account for data in other languages.
  • The metric requires a valid Dataset object, indicating a dependency condition that limits its broader applicability.