Ratings and Reputation Spec (Notes)


Arumble Elements Breakdown

sentiment rating

how strong you feel about the subject

sentiment rating – design

  • structured sentiment is a means of defining a scale for expressing sentiment – this may be grades
  • “adaptability” is important when initially integrating into (or replacing) existing sets of data or methods of conveying structured sentiment already in-place

sentiment rating – context

  • ratings can be related to an article about a topic or combination of topics, where the subject is not concrete.
  • ratings can be relative to a subject that is also an attribute in common among other subjects.
  • ratings can be relative to the “relationship” between two subjects
  • ratings can be relative to an experience related to a subject
  • ratings can be relative to a previous rating


  • words or phrases that describe the subject or attributes of the subject
  • can be ambiguous to attributes, but is unstructured user input
  • use semantics/dictionaries to classify tag meaning as a process for normalization?

tag – relevance

how much/little tag applies to subject

tag – sentiment

how strong you feel about the attribute of the subject the tag describes

tag-attribute inference

  • determine if a tag on a subject actually indicates an attribute of the subject
  • use knowledge of subject design (attribute structure) to link tag to attribute, marked as inference for verification or exclusion in statistical calculation?
  • use semantics/dictionaries to identify relationships

experience indicators

provide way to indicate if tags/sentiment is based on anticipation/expectation or experience with subject

compare subjects

subjects are typically a composite of attributes, some explicitly defined (which can be subjects themselves) and some derived from tags

compare sentiment

compare how the sentiment of one iteration of a subject compares to another, how sentiment changed regarding particular attributes


rank subjects relative to eachother to indicate “preference”

timeline – trends in tags/sentiment

how do tags or sentiment change over time regarding one subject

timeline – trends in subject iteration

how do tags or sentiment change from one subject/attribute to the next iteration

Arumble Platform Community

engage via point system

  • allow users to gain credibility based on participation
  • allow verification via tools provided to the community that let users flag odd submissions or to thumb up/down content


lists, such as “best movies of the year” – ranked or unranked (can be ranked by overall ranking/rating or specifically for this grouping)

trend data


relationships – recommendations

  • allow users to annotate subjects
  • create “Reminds me of…” feature to draw parallels
  • provide tools to compare subjects side-by-side, i.e. such as video clips next to each other to show similarities, or with audio clips or photos or quotes, etc….


Anticipation rating based on feedback and also anticipation chatter


Rating styles (structure/orientation)

  • “star” rating scale associated with sentiment words (like Netflix stars)
  • word describing sentiment with “star” rating scale indicating severity of sentiment towards that word (“inverse” of Netflix, stars associated with one word)
  • short 2 or 3-word phrases indicating sentiment toward an aspect or quality of a subject

Rating features (usability)

  • auto-complete for sentiment words, show “tag cloud” of related words and their frequency of use

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