x = , ⋯ ,

x

-score

x

rating profile

δ-score

σ

δ-score

δ

λ-score

= ,⋯ ,

w = , ⋯ ,

p

-score

p

p = , ⋯ ,

holistic approach

σ

δ-score

δ

probability profile

σ

δ-score

δ

frequency profile

f

-score

f

f = , ⋯ ,

analytic approach

standard polarity reverse polarity

weight vector

score vector

reference model

© SQUIRE Research Institute Paul Hubert Vossen 2013-05-10

rating version

frequency version

probability version

score qualification : -curves (δ=0,70) score qualification : σ-curves (δ=0,70)

quality criteria: finite granularity

reverse polaritystandard polarity

score aggregation : random weights score aggregation : equal weights

reference model

© SQUIRE Research Institute Paul Hubert Vossen 2013-05-10

→→

× ≝ 1 − 1 −

1 − 1 −× σ

quality criterion(infinite granularity)

score

δ

impact

δ-score

score qualification

rating version

rating profile

, ⋯⋯⋯ ,x

tolerance

↔ x ≝ x + 1 − xx x

x 1 −1

∑ ∑ , − 1

1,∑

probability version

tolerance

p 1 −1− 1

1,∑

p1− 1

1,∑ p ↔ p ≝ p + 1 − p

probability profile

, ⋯⋯⋯ ,p

≝ 1 − ⊕= 1

• 1 −↔ ≝ + 1 −

lenience

1 − 1 − ≝ ⊕= 1

•, ⋯ ,

scores

s , ⋯ ,

weights

w

λ-score

score aggregation

standard polarity

reverse polarity

− −

+ = 1

−−

+ = 1

score

-score

frequency version

tolerance

f ↔ f ≝ f + 1 − f

frequency profile

, ⋯⋯⋯ ,f f 1 −1

∗ ,∑ − 1∗ ,

f1

∗ ,∑ − 1∗ ,

quality criteria(finite granularity)

tight grading

loose grading tight grading

loose grading

lowest grading

highest grading

highest grading

lowest grading

unbiased grading

¼ ¾0 1½

unbiased grading

¼1 0½¾

highest grademiddle grade

+ < 1 > 1

lowest gradecurvature

calibrating

≤

reference model

© SQUIRE Research Institute Paul Hubert Vossen 2013-05-10