1. A computer-implemented method of classifying a keyword in a network, the method comprising: acquiring resource contents from a plurality of resources in the network based on at least one search of the keyword, wherein the at least one search generates a plurality of search results related to the keyword; determining performance metrics for each of the plurality of resources in the network; with a classification module, executing on one or more computing devices, automatically: identifying a plurality of candidate categories, comprising: converting the plurality of search results related to the keyword into a plurality of search vectors, wherein each of the plurality of search results indicates a related resource of the plurality of resources in the network; converting the plurality of resources into a plurality of category vectors, wherein each of the plurality of resources is classified in one or more categories of a set of categories; determining, for the plurality of category vectors, a plurality of similarity values indicating similarity to the plurality of search vectors by determining cosine similarities between each of the plurality of category vectors and each of the plurality of search vectors; extracting, for each of the plurality of search results or plurality of resources, one or more terms relevant to the related resource; removing, for each of the plurality of search results or plurality of resources, one or more terms irrelevant to the related resource; producing word embeddings using a Word2Vec model, continuous bag-of-words model or continuous skip-gram model; reducing dimensionality of the vectors of the plurality of search vectors or resource vectors using nonlinear dimensionality reduction or t-distributed stochastic neighbor embedding; and selecting, for the plurality of search result, a pre-determined number of search results or per search result, a pre-determined number of candidate categories having higher similarity values within the plurality of similarity values; processing the plurality of candidate categories; and classifying the keyword by selecting the candidate category having a highest similarity value within the plurality of similarity values by determining a keyword cosine similarity between the candidate category and the keyword.