13. A non-transitory, computer-readable medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising: selecting a first article of interest from a plurality of articles, the article being associated with a first plurality of comments previously provided from one or more entities having accessed the article; extracting the first plurality of comments; in response to a determination that the first plurality of comments exceed a comment threshold that defines a minimum number of comments to use in determining a native context, determining the native context for the selected first article by: determining a term-comment matrix for the first plurality of comments, the term-comment matrix identifying a plurality of terms found in the first plurality of comments; applying a matrix factorization to the term-comment matrix to obtain the context-aware topic distribution of topics associated with the first plurality of comments; determining a context-aware feature for a comment selected from the extracted first plurality of comments based on the context-aware topic distribution and a similarity between the selected comment and the first article; applying a text classifier to the extracted one or more comments using the determined context-aware feature, the text classifier providing an indication of whether a given comment from the extracted plurality of comments is irrelevant; and in response to the applied text classifier, taking an action on the given comment based on the provided indication; and in response to a determination that the extracted one or more comments do not exceed the comment threshold, determining a transferred context for the selected first article by: extracting a second plurality of comments from a subset of articles selected from the plurality of articles, the extracted second plurality of comments being topically similar to the extracted first plurality of comments, wherein the subset of articles includes articles other than the first article; combining the extracted first plurality of comments and the extracted second plurality of comments to obtain a third plurality of comments; applying a matrix factorization to the third plurality of comments to determine context-dependent semantics of the first plurality of comments; determining a transferred context-aware feature for the first plurality of comments using the determined context-dependent semantics and the third plurality of comments; applying the text classifier to the extracted first plurality of comments using the determined transferred context-aware feature, the text classifier providing an indication whether a given comment from the extracted first plurality of comments is irrelevant; and in response to the applied text classifier, taking an action on the given comment based on the provided indication.
13. A non-transitory, computer-readable medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising: selecting a first article of interest from a plurality of articles, the article being associated with a first plurality of comments previously provided from one or more entities having accessed the article; extracting the first plurality of comments; in response to a determination that the first plurality of comments exceed a comment threshold that defines a minimum number of comments to use in determining a native context, determining the native context for the selected first article by: determining a term-comment matrix for the first plurality of comments, the term-comment matrix identifying a plurality of terms found in the first plurality of comments; applying a matrix factorization to the term-comment matrix to obtain the context-aware topic distribution of topics associated with the first plurality of comments; determining a context-aware feature for a comment selected from the extracted first plurality of comments based on the context-aware topic distribution and a similarity between the selected comment and the first article; applying a text classifier to the extracted one or more comments using the determined context-aware feature, the text classifier providing an indication of whether a given comment from the extracted plurality of comments is irrelevant; and in response to the applied text classifier, taking an action on the given comment based on the provided indication; and in response to a determination that the extracted one or more comments do not exceed the comment threshold, determining a transferred context for the selected first article by: extracting a second plurality of comments from a subset of articles selected from the plurality of articles, the extracted second plurality of comments being topically similar to the extracted first plurality of comments, wherein the subset of articles includes articles other than the first article; combining the extracted first plurality of comments and the extracted second plurality of comments to obtain a third plurality of comments; applying a matrix factorization to the third plurality of comments to determine context-dependent semantics of the first plurality of comments; determining a transferred context-aware feature for the first plurality of comments using the determined context-dependent semantics and the third plurality of comments; applying the text classifier to the extracted first plurality of comments using the determined transferred context-aware feature, the text classifier providing an indication whether a given comment from the extracted first plurality of comments is irrelevant; and in response to the applied text classifier, taking an action on the given comment based on the provided indication. 14. The non-transitory, computer-readable medium of claim 13 , wherein the method further comprises: determining a transferred context-aware topic distribution of topics from the third plurality of comments, wherein the determining of the transferred context-aware feature is based on the determined transferred context-aware topic distribution.