1. A method comprising: parsing a text into one or more words; filtering the one or more words to obtain one or more filtered words prior to classification of the text, the filtering comprising: determining an amount of variation of frequencies of appearance associated with a word of the one or more words across a plurality of categories; and filtering out the word in response to determining that the amount of variation is less than a first variation threshold and greater than a second threshold, wherein the second threshold is greater than zero; determining a word vector in a pre-constructed spherical space model for each filtered word of the one or more filtered words, a number of dimensions of the spherical space model being equal to a number of the plurality of categories; determining a distance between a sum of word vectors of the one or more filtered words and a respective category vector associated with each category of the plurality of categories in the spherical space model, determining the distance including accumulating normalized word frequency values of the one or more filtered words to obtain a normalized word vector sum; and classifying the text into one or more categories based at least in part on the determined distance associated with each category, classifying the text including classifying the text into a category corresponding to a largest component of the normalized word vector sum.
1. A method comprising: parsing a text into one or more words; filtering the one or more words to obtain one or more filtered words prior to classification of the text, the filtering comprising: determining an amount of variation of frequencies of appearance associated with a word of the one or more words across a plurality of categories; and filtering out the word in response to determining that the amount of variation is less than a first variation threshold and greater than a second threshold, wherein the second threshold is greater than zero; determining a word vector in a pre-constructed spherical space model for each filtered word of the one or more filtered words, a number of dimensions of the spherical space model being equal to a number of the plurality of categories; determining a distance between a sum of word vectors of the one or more filtered words and a respective category vector associated with each category of the plurality of categories in the spherical space model, determining the distance including accumulating normalized word frequency values of the one or more filtered words to obtain a normalized word vector sum; and classifying the text into one or more categories based at least in part on the determined distance associated with each category, classifying the text including classifying the text into a category corresponding to a largest component of the normalized word vector sum. 4. The method as recited in claim 1 , wherein filtering the one or more words further comprises retaining another word of the one or more words which frequencies of appearance vary across the plurality of categories at an amount greater than the first variation threshold.