1. A computerized method for the analysis of textual data, comprising: receiving, from one or more memories at one or more processors, textual data to be analyzed; using the one or more processors, formatting the textual data for subsequent analysis; using the one or more processors, applying a probabilistic topic model to the textual data to extract a set of semantically meaningful topics that collectively describe all or a portion of the textual data; using a keyword weighting module executed on the one or more processors, generating a topic cloud view representing the topics as a tagcloud with each being associated with a plurality of keywords; using a topic ordering module executed on the one or more processors, generating a document distribution view representing a distribution of all or a portion of the textual data across multiple topics; using a document entropy calculation module executed on the one or more processors, generating a document scatterplot view representing how many topics are attributable to all or a portion of the textual data; using a temporal topic trend calculation module executed on the one or more processors, generating a temporal view representing changes in the occurrence of topics over time in relation to all or a portion of the textual data; and displaying one or more of the topic cloud view, the document distribution view, the document scatterplot view, and the temporal view to a user in the analysis of all or a portion of the textual data.