Output - Aster Analytics

Teradata AsterĀ® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Language
English (United States)
Last Update
2018-04-17
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uce1497542673292.ditamap
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AA-notempfilter_pdf_output.ditaval
dita:id
B700-1022
lifecycle
previous
Product Category
Software
EvaluateSentimentExtractor Output Table Schema
Column Name Data Type Description
evaluation_result VARCHAR Reports these values:
  • Positive record (total relevant, relevant, total retrieved)
  • Recall and precision
  • Negative record (total relevant, relevant, total retrieved)
  • Recall and precision
  • Positive and negative record (total relevant, relevant, total retrieved)
  • Recall and precision

Given these definitions:

  • POS_EXPECT = count of rows in which observed sentiment is POS
  • POS_RETURN = count of rows in which actual sentiment is POS
  • POS_TRUE = count of rows in which both observed and actual sentiment are POS
  • NEG_EXPECT = count of rows in which observed sentiment is NEG
  • NEG_RETURN = count of rows in which actual sentiment is NEG
  • NEG_TRUE = count of rows in which both observed and actual sentiment are NEG
  • NEU_EXPECT = count of rows in which observed sentiment is NEU
  • NEU_RETURN = count of rows in which actual sentiment is NEU
  • NEU_TRUE = count of rows in which both observed and actual sentiment are NEU

Precision and recall are calculated as follows:

  • Positive sentiment:
    • Precision = POS_TRUE / POS_RETURN
    • Recall = POS_TRUE / POS_EXPECT
  • Negative sentiment:
    • Precision = NEG_TRUE / NEG_RETURN
    • Recall = NEG_TRUE / NEG_EXPECT
  • Neutral sentiment:
    • Precision = NEU_TRUE / NEU_RETURN
    • Recall = NEU_TRUE / NEU_EXPECT
  • All sentiment:
    • Precision = (POS_TRUE + NEG_TRUE + NEU_TRUE) / (POS_RETURN + NEG_RETURN + NEU_RETURN)
    • Recall = (POS_TRUE + NEG_TRUE + NEU_TRUE) / (POS_EXPECT + NEG_EXPECT + NEU_EXPECT)