  In this paper we perform a comparative analysis of three models for feature
representation of text documents in the context of document classification. In
particular, we consider the most often used family of models bag-of-words,
recently proposed continuous space models word2vec and doc2vec, and the model
based on the representation of text documents as language networks. While the
bag-of-word models have been extensively used for the document classification
task, the performance of the other two models for the same task have not been
well understood. This is especially true for the network-based model that have
been rarely considered for representation of text documents for classification.
In this study, we measure the performance of the document classifiers trained
using the method of random forests for features generated the three models and
their variants. The results of the empirical comparison show that the commonly
used bag-of-words model has performance comparable to the one obtained by the
emerging continuous-space model of doc2vec. In particular, the low-dimensional
variants of doc2vec generating up to 75 features are among the top-performing
document representation models. The results finally point out that doc2vec
shows a superior performance in the tasks of classifying large documents.
