TECHNICAL FIELD

Aspects and implementations of the present disclosure relate to data processing, and more specifically, to identifying subjective attributes of entities.

BACKGROUND

User-generated content is becoming pervasive on the World Wide Web due to the increasing popularity of social networks, blogs, review websites, etc. Such content is often in the form of comments (e.g., a comment by a first user pertaining to content shared by a second user within a social network, a user comment in response to an article in a columnist's blog, a user comment pertaining to a video clip posted on a content hosting website, etc.), user reviews (e.g., of products, movies, etc.), user actions (e.g., Like!, Dislike!, +1, sharing, bookmarking, playlisting, etc.), and so forth.

SUMMARY

The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.

In an aspect of the present disclosure, a method for identifying and predicting subjective attributes for entities (e.g., media clips, images, newspaper articles, blog entries, persons, organizations, commercial businesses, etc.) is provided. The method includes identifying a first set of subjective attributes for a first entity based on a reaction to the first entity (e.g., comments on a website, a demonstration of approval of the first entity (e.g., “Like!, etc.), sharing the first entity, bookmarking the first entity, adding the first entity to a playlist, etc.). The method may then include training a classifier (e.g., a support vector machine [SVM], AdaBoost, a neural network, a decision tree, etc.) on a set of input-output mappings, where the set of input-output mappings comprises an input-output mapping whose input is based on a feature vector for the first entity and whose output is based on the first set of subjective attributes. A feature vector for a second entity may then be provided to the trained classifier to obtain a second set of subjective attributes for the second entity.

In some implementations, an apparatus comprising a memory and a processor is provided to identify and predict subjective attributes for entities. In other implementations, a computer readable storage medium is provided that has instructions that cause a computer system to perform operations including: identifying a first set of subjective attributes for a first entity based on a reaction to the first entity; obtaining a first feature vector for the first entity; training a classifier on a set of input-output mappings, wherein the set of input-output mappings comprises an input-output mapping whose input is based on the first feature vector and whose output is based on the first set of subjective attributes; obtaining a second feature vector for a second entity; and providing to the classifier, after the training, the second feature vector to obtain a second set of subjective attributes for the second entity.

