SOCIAL CONTENT FILTER TO ENHANCE SENTIMENT ANALYSIS

摘要

Techniques are disclosed for filtering and analyzing social network content so that consumer sentiment can be gauged more accurately and efficiently. In certain embodiments social network content can be filtered so that individual content items can be identified as comprising neutral, sentiment bearing, spam or foreign language content. Such filtering can be performed by marking certain features that are indicative of a particular type of content, and then using machine learning systems to classify individual content items based on the marked features. A portion of the filtered content, such as only the items containing sentiment bearing content, can then be subjected to sentiment analysis. The results of this sentiment analysis can be presented to a social network campaign manager via a sentiment browser interface, optionally with the underlying filtered content. This allows the campaign manager to easily view the results of the sentiment analysis with the filtered social network content.

主权项

1. A computer-implemented content filtration method for analyzing and filtering content generated via an online social network, the method comprising:
receiving a plurality of social network content items from a social network server, wherein each of the plurality of content items can be characterized as one of a plurality of content types, the plurality of content types including sentiment bearing social network content and spam content; evaluating a particular one of the plurality of content items for applicability of a plurality of features; generating a feature vector corresponding to the particular content item, the feature vector providing a representation of a subset of the plurality of features which are evaluated as being applicable to the particular content item; selectively masking a feature included in the subset, wherein the masked feature is selected based on a correlation between the masked feature and a selected content type that is to be excluded through the content filtration method; and characterizing the particular content item as one of the plurality of content types based on unmasked features in the feature vector.