Methods and apparatus for person-centric multichannel opinion mining in data lakes

Abstract

Person-centric multi-channel opinion mining is performed in a single data repository, such as a data lake. An exemplary method comprises obtaining multi-channel heterogeneous data from a plurality of channels; identifying entities that are targets of opinion information across the plurality of channels; extracting a plurality of user identities from the plurality of channels; aligning the plurality of extracted user identities across the plurality of channels to link common user identities; identifying the entities that are targets of the opinion information of the extracted user identities; linking opinion information of the extracted user identities with a user identity associated with an opinion holder that expressed the opinion information; determining whether the opinion information comprises a positive or negative opinion; and providing a summary of the opinion information of a given opinion holder. Sentiment polarity classification algorithms optionally determine whether opinion information comprises a positive or negative opinion and assign a polarity score. An influencer score of the opinion holder is optionally assigned to the opinion information.