Abstract: Systems of the present disclosure may ingest content from a plurality of data sources with the content including ingested documents referencing entities and events relevant to the ESG signals. The content may be stored in a content database. The System may also identify metadata and a body of text associated with each document to produce a set of preprocessed documents. An entity may be tagged to a first preprocessed document from the set of preprocessed documents, and the document may include a first document identifier. The System may generate an event score related to a first ESG signal including a direction and a magnitude associated with an event identified in the body of text. The event score may be tagged to the document. The system may write to an unstructured data set the document identifier in association with the tagged entity and the tagged event score for delivery.

Abstract: Methods and systems of assessing aggregate sentiment over a plurality of time increments of a time period are provided. A maximum aggregation factor that is associated with a particular time period is assigned. A plurality of time increments over the time period are received. For each time increment, the BISV is subtracted from the ISV to form a BISV/ISV difference value. The BISV/ISV difference value is normalized by dividing by the maximum possible difference, thereby determining a modulator. For each time increment, a value is assigned to a recency of the particular time increment to a most recent incremental sentiment value update event, thereby determining a decay factor. The maximum aggregation factor associated with a particular time period is modulated by multiplying a determined modulator and a determined decay factor associated with each time increment within the evaluated time interval.

Abstract: A data analysis system for measuring a materiality feature of interest is disclosed. The system includes a computing cluster ingesting content comprising a plurality of observables relevant to an entity, wherein each observable is related to at least one feature of interest. The system further includes an extraction engine running on the computing cluster and tagging the observables with an entity identifier in response to the observables referencing at least one of an entity, a tradename associated with the entity, or product associated with the entity. Additionally, the system includes an analysis engine running on the computing cluster and tagging an observable in response to the feature of interest being related to the observable. In one embedment, the analysis engine measures the materiality of the feature of interest to the entity by counting a number of observables from the plurality of observables tagged with the entity identifier.

Abstract: Methods and systems of assessing aggregate sentiment over a plurality of time increments of a time period are provided. A maximum aggregation factor that is associated with a particular time period is assigned. A plurality of time increments over the time period are received. For each time increment, the BISV is subtracted from the ISV to form a BISV/ISV difference value. The BISV/ISV difference value is normalized by dividing by the maximum possible difference, thereby determining a modulator. For each time increment, a value is assigned to a recency of the particular time increment to a most recent incremental sentiment value update event, thereby determining a decay factor. The maximum aggregation factor associated with a particular time period is modulated by multiplying a determined modulator and a determined decay factor associated with each time increment within the evaluated time interval.