  Text classification has become indispensable due to the rapid increase of
text in digital form. Over the past three decades, efforts have been made to
approach this task using various learning algorithms and statistical models
based on bag-of-words (BOW) features. Despite its simple implementation, BOW
features lack semantic meaning representation. To solve this problem, neural
networks started to be employed to learn word vectors, such as the word2vec.
Word2vec embeds word semantic structure into vectors, where the angle between
vectors indicates the meaningful similarity between words. To measure the
similarity between texts, we propose the novel concept of word subspace, which
can represent the intrinsic variability of features in a set of word vectors.
Through this concept, it is possible to model text from word vectors while
holding semantic information. To incorporate the word frequency directly in the
subspace model, we further extend the word subspace to the term-frequency (TF)
weighted word subspace. Based on these new concepts, text classification can be
performed under the mutual subspace method (MSM) framework. The validity of our
modeling is shown through experiments on the Reuters text database, comparing
the results to various state-of-art algorithms.
