1. A computer-implemented method comprising: obtaining a matrix of words and locations; generating a locality-sensitive hash (LSH) function; identifying a row vector corresponding to a word in the matrix, the row vector comprising a plurality of location dimensions; generating a sparse row vector that corresponds to the row vector by performing location sampling relative to a geospatial distribution of the word, wherein performing location sampling comprises selecting a subset of dominant location dimensions from the plurality of location dimensions in the row vector to retain in the sparse row vector and discarding the remaining location dimensions, and wherein selecting a subset of dominant location dimensions comprises determining that the word corresponding to the row vector occurs more frequently in connection with the dominant location dimensions than the discarded remaining location dimensions; generating a sparse LSH function using the LSH function and the sparse row vector; generating a signature for the sparse row vector using the sparse LSH function; determining a clustering bucket to which the signature corresponds; adding the word to the clustering bucket, receiving input data, generating an input signature corresponding to the input data; determining that the input signature matches a signature associated with the clustering bucket; determining a geographical location from metadata associated with the clustering bucket; and geotagging the input data by associating the geographical location with the input data.