Abstract: A method for facilitating a construction of a rank-ordered list of companies based on a theme is provided. The method includes identifying a plurality of companies associated with at least one stock exchange; determining search terms that relate to the theme; and constructing a query based on the search terms. For each company, the query is applied to a first set of company-specific textual sources and documents, in order to determine a textual relevance score, and the query is also applied to a second set of sources that relate to company-specific revenue data, in order to determine a revenue exposure score. The two scores are then combined into a composite score, and the companies are rank-ordered based on the respective composite scores. The rank-ordered list may be used for constructing a thematic investment portfolio.

Abstract: Systems and methods for content filtering based on relevance of publications are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for content filtering of publications may include: (1) receiving a trained neural network, the trained neural network being trained to predict a relevance probability of a publication and comprising a plurality of word vectors, each word vector having a set of trained weights; (2) receiving a publication for evaluation; (3) identifying textual data in the publication; (4) identifying a plurality of sentences in the textual data; (5) identifying a plurality of words in the plurality of sentences; (6) transforming each of the words into word vectors; (7) applying the trained neural network to predict the relevance probability for the publication using the word vectors; and (8) outputting the relevance probability for the publication.