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Association Scores: Is there a specific threshold that indicates a strong relationship?

Last Updated: Aug 18, 2017
Here are guidelines for determining the meaning of Topic-Topic association score thresholds:

Topic-Topic association scores range from -1.00 (no relationship) to 1.00 (the relationship between a concept and itself). The following guidelines will help you determine the meaning of association scores:
  • A >0.70 usually indicates a definitional relationship (e.g., awesome/amazing or screen/display or alert/notification or uninstall/reinstall). If two terms are not synonymous (or very close in meaning) and have a >0.70 score, this indicates a strong and meaningful relationship between concepts.
  • A >0.20 and <0.70 association score is often where the most interesting findings lie in a project. This is where you should focus your attention in any project. These concepts are significantly highly related.
  • A >0.0 and <0.20 association score indicates that two concepts have a weak association in the data.
  • 0.00 score indicates no meaningful relationship between two concepts beyond random chance.
  • A <0.0 association score (or negative association score) indicates that two topics are unrelated to one another in your dataset.
NOTE: Negative association scores do NOT infer negative sentiment.

Here are guidelines for determining the meaning of Topic-Subset association score thresholds:
  • Topic-Subset association scores generally fall within a smaller range that is closer to 0.0 as compared to Topic-Topic scores. Topic-Subset relationships typically fall between -0.30 to 0.30. Scores that are much higher than this range are extremely significant.
  • Topic-Subset association scores also tend to have less variance from one another. A 0.05 point differential between two Topic-Subset association scores is meaningful and interesting. Differences less than 0.05 scores between two Topic-Subset association scores should be evaluated with some caution as they may be influenced by varying document volume of each subset or topic.

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