Abstract: Feedback attribution includes receiving a feedback item pertaining to an entity. It further includes receiving information pertaining to a plurality of candidate sources of the feedback item. It further includes determining a probabilistic mapping between the feedback item and the plurality of candidate sources. Determining the probabilistic mapping comprises determining, for each candidate source in the plurality of individuals, a likelihood that the feedback is attributed to a given candidate source.

Abstract: Probabilistic employee attribution includes receiving a feedback item pertaining to an organization. It further includes extracting a named entity from text of the feedback item. It further includes determining a list of candidate employees of the organization. It further includes determining, for each candidate employee in the list of candidate employees, a corresponding probability that the candidate employee matches to the named entity extracted from the text of the feedback item. It further includes providing output based at least in part on the probability that the candidate employee matches to the named entity extracted from the text in the feedback item.

Abstract: Macro-Attribution of feedback to solicitation includes determining historical time series information pertaining to collected feedback items. It further includes determining historical time series information pertaining to feedback solicitation. It further includes generating, based at least in part on the historical time series information pertaining to the collected feedback items and the historical time series information pertaining to the feedback solicitation, a macro-attribution model usable to estimate an expected number of feedback items to be received for a time period. It further includes receiving a set of feedback items for the time period. It further includes using the macro-attribution model to determine, for the feedback items received for the time period, an estimate of an amount of feedback that was generated organically, and an estimate of an amount of feedback that was generated by solicitation.

Abstract: Entity scoring calibration includes receiving values for a metric by which to calibrate a reputation scoring model. The reputation scoring model is usable to determine a reputation score. The reputation scoring model to be calibrated is based at least in part on a set of model parameters associated with reputation scoring components. It further includes receiving a plurality of feedback items pertaining to one or more entities. It further includes calibrating the reputation scoring model at least in part by adjusting at least some of the model parameters associated with the reputation scoring components such that reputation scores generated by the calibrated reputation scoring model track the values for the metric.

Abstract: Virality cause determination includes receiving a plurality of historical feedback items. It further includes bucketing a first feedback item in the plurality of historical feedback items into a first partition of feedback items, and bucketing a second feedback item in the plurality of historical feedback items into a second partition of feedback items. The second partition of feedback items is a baseline partition of feedback items. It further includes determining a set of candidate patterns present in the first partition of feedback items. It further includes determining whether a candidate pattern in the set of candidate patterns is disproportionately associated with the first partition of feedback items relative to the baseline partition of feedback items.

Abstract: Impact-based strength and weakness determination includes receiving a plurality of industry-wide feedback items, the industry-wide feedback items pertaining to a plurality of entities associated with an industry. It further includes, based at least in part on an evaluation of the plurality of industry-wide feedback items, generating an industry-wide reputation scoring model usable to determine an expected reputation score for a typical entity in the industry based at least in part on a combination of one or more reputation score components. Generating the industry-wide reputation scoring model comprises determining (1) a baseline reputation score, and (2) an expected impact of a reputation score component on reputation scoring for the typical entity in the industry.

Abstract: Virality cause determination includes receiving a plurality of historical feedback items. It further includes bucketing a first feedback item in the plurality of historical feedback items into a first partition of feedback items, and bucketing a second feedback item in the plurality of historical feedback items into a second partition of feedback items. The second partition of feedback items is a baseline partition of feedback items. It further includes determining a set of candidate patterns present in the first partition of feedback items. It further includes determining whether a candidate pattern in the set of candidate patterns is disproportionately associated with the first partition of feedback items relative to the baseline partition of feedback items.

Abstract: Impact-based strength and weakness determination includes receiving a plurality of industry-wide feedback items, the industry-wide feedback items pertaining to a plurality of entities associated with an industry. It further includes, based at least in part on an evaluation of the plurality of industry-wide feedback items, generating an industry-wide reputation scoring model usable to determine an expected reputation score for a typical entity in the industry based at least in part on a combination of one or more reputation score components. Generating the industry-wide reputation scoring model comprises determining (1) a baseline reputation score, and (2) an expected impact of a reputation score component on reputation scoring for the typical entity in the industry.

Abstract: Entity scoring calibration includes receiving values for a metric by which to calibrate a reputation scoring model. The reputation scoring model is usable to determine a reputation score. The reputation scoring model to be calibrated is based at least in part on a set of model parameters associated with reputation scoring components. It further includes receiving a plurality of feedback items pertaining to one or more entities. It further includes calibrating the reputation scoring model at least in part by adjusting at least some of the model parameters associated with the reputation scoring components such that reputation scores generated by the calibrated reputation scoring model track the values for the metric.

Abstract: Impact-based strength and weakness determination includes receiving a plurality of industry-wide feedback items, the industry-wide feedback items pertaining to a plurality of entities associated with an industry. It further includes, based at least in part on an evaluation of the plurality of industry-wide feedback items, generating an industry-wide reputation scoring model usable to determine an expected reputation score for a typical entity in the industry based at least in part on a combination of one or more reputation score components. Generating the industry-wide reputation scoring model comprises determining (1) a baseline reputation score, and (2) an expected impact of a reputation score component on reputation scoring for the typical entity in the industry.

Abstract: Entity scoring calibration includes receiving values for a metric by which to calibrate a reputation scoring model. The reputation scoring model is usable to determine a reputation score. The reputation scoring model to be calibrated is based at least in part on a set of model parameters associated with reputation scoring components. It further includes receiving a plurality of feedback items pertaining to one or more entities. It further includes calibrating the reputation scoring model at least in part by adjusting at least some of the model parameters associated with the reputation scoring components such that reputation scores generated by the calibrated reputation scoring model track the values for the metric.

Abstract: The generation of an online reputation report for an individual is disclosed. First, an online reputation assessment is performed for an individual. Then, a report is generated. The report includes an individualized recommendation based on the online reputation assessment. The recommendation can be directed to the individual and can also be directed to an entity other than the individual.

Abstract: An online reputation assessment of an individual is performed. A reputation score is determined based on the performed online reputation assessment. The reputation score is provided as output. In some cases, the reputation score is provided to an entity that is not the individual, for example, based on the receipt of an authorization from the individual to disclose the score to the entity.

Abstract: An online reputation assessment of an individual is performed. A reputation score is determined based on the performed online reputation assessment. The reputation score is provided as output. In some cases, the reputation score is provided to an entity that is not the individual, for example, based on the receipt of an authorization from the individual to disclose the score to the entity.

Abstract: The generation of an online reputation report for an individual is disclosed. First, an online reputation assessment is performed for an individual. Then, a report is generated. The report includes an individualized recommendation based on the online reputation assessment. The recommendation can be directed to the individual and can also be directed to an entity other than the individual.