'Opinion' is an attribute option that becomes available when 'Reporting by' a Concept dimension. Opinion functions are ways to measure the amount of positive and/or negative sentiment towards a target topic.

For example, the following verbatim mentions a problem with an airlines seating.


"The air hostess was friendly and nice but my seat was very small."


This record would be counted as 'having an opinion' about 'Seating'. It would contribute to the 'Opinion' measure as a count of 1 when asking for Opinion for the 'Seating' concept. In this example 'Seating' is what we call the 'target topic'. When no target topic is available opinion functions return based on any target.

In Ipiphany, we label negative sentiments as Problems and positive sentiments as Benefits.

The functions that are available for 'Opinion' are as follows:

Function

Description

Used to answer

Opinion

A count of records containing an opinion about the target topic.

Which topics do people have the most opinions about?

Opinion %

The percentage of records with an

opinion about the target topic within the current dataset filter.

Which topics do people proportionally have the most opinions about?

Net Opinion

The Benefit % minus the Problem %.

Which topics have the most problem or benefit bias?

Net Opinion (Topic)

The Benefit % (Topic) minus the Problem % (Topic).

Which topics have the most concentrated problem or benefit bias?

Problem

A count of the records mentioning a problem with the target topic.

Which topics have the most problems?

Problem %

The percentage of records mentioning a problem about the target topic within the current dataset filter.

Which topics proportionally have the most problems?

Problem % (Topic)

The percentage of records mentioning a problem with the target topic within all mentions of that topic.

Which topics have the most concentrated amount of problems?

Benefit

A count of the records mentioning a benefit of the target concept.

Which topics have the most benefits?

Benefit %

The percentage of records mentioning a benefit of the target topic within the current dataset filter.

Which topics proportionally have the most benefits?

Benefit % (Topic)

The percentage of records mentioning a benefit of the target topic within all mentions of that topic.

Which topics have the most concentrated amount of benefits?

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