System and method for automatically classifying text

In a system including perspectives and categories, each perspective comprising at least one category representative of that perspective, a method for constructing a classifier to classify at least one item across multiple perspectives, the method including:

• associating at least one feature with each category, in which each feature is configured for being detected in at least a portion of the at least one item for classification of that item;
• determining an initial weight indicating a degree of association between each associated feature and category;
• and
• in which weights for a category are initially related to weights for other categories of the same perspective but are initially substantially unrelated to weights for categories in different perspectives.

In a system comprising perspectives and categories, each perspective comprising at least one category representative of that perspective, the system also comprising weights, each weight indicating a degree of an association between a feature and a category, a method for classifying at least one item across multiple perspectives, the method comprising:

• identifying feature instances in the items;
• representing, for each item, which features were identified in that item and the number of instances each such feature was identified in that item;
• computing, for each item, a category score for each category associated with at least one feature identified in that item, the computing using the weight associating the category and the at least one feature identified in that item;
• selecting one or more categories to represent each perspective according to the category scores;
• and
• classifying the items across the selected categories representing the multiple perspectives.

In a system for classifying items to categories, a method including:

• receiving user-input creating user-defined associations between classification features and categories;
• statistically determining machine-defined associations that are capable of being different from the user-defined associations;
• and
• classifying items to the categories using weights corresponding to the user-defined associations and the machine-defined associations.