# The Influence of Feature Representation of Text on the Performance of Document Classification
## 4 Results
### Discussion  
Taken together, the presented results identify two top-performing document representation models; the traditionally and commonly used bag-of-words, and the more recent doc2 vec model. The findings are consistent regardless of the performance evaluation metrics. The standard variant of the bag-of-words model often used in the text mining studies [2; 62] is bow-TFIDF. However, our results show that the TF variant is often better or comparable to bow-TFIDF. The only performance metrics where the latter outperforms TF is the recall. When it comes to the variants of doc2 vec, we have an important result, not reported in related studies. Namely, the low-dimensional variants of doc2 vec are better performers than the high-dimensional ones. In our results, the phase switch is observed at 75 features. While this might be an artifact of the data sets and tasks selected in this study, it is a general pattern that consistently appears among all of them.  
The word2 vec model is mostly ranked in the middle of the list. The top-ranked variants of word2 vec correspond to the feature vector dimensionality of 50 and 75, the other two settings of 25 and 100 underperform in almost all the experiments. Early on in the experiments, we noticed that the higher-dimensional variants (>100) of word2 vec (just as the ones for doc2 vec) have a deteriorated performance. Note that the lower performance of the word2 vec document representation model (when compared to doc2 vec) might be due to the averaging method used [42]. On the other hand, (an extension of) doc2 vec has already been shown to perform well in the document classification task [9].  
The network-based model is systematically underperforming regardless of the selected task or evaluation measure. Still, some additional remarks should be noted. The number of features used in all three network-based model variants with averaging, quartiles and histograms are 19, 68 and 128, respectively, which is lower than in other models (see Table 2 for details). Although GOW is lagging behind in all the rankings, in some (rare) occasions, it can still be the representation of choice, especially if we look for a low-dimensional document representation model robust to noise. Namely, note that the network-based model requires no extensive text pre-processing, which can be useful when aiming at efficient implementations of document classification procedures, especially for low-resources languages, which lack elaborate text preprocessing tools.  
Finally, the analysis of the results with respect to the criteria of the document, vocabulary and class label set size does not reveal clear results with several notable exceptions, addressed next. Regarding the document size, bag-of-words is the preferred model for smaller documents regardless of the evaluation metrics, while for larger documents doc2 vec has a slight advantage. Additionally, when it comes to choosing between word2 vec and doc2 vec, the latter seems to be consistently the better choice, except when observing accuracy on data sets with larger vocabularies and tasks with a moderate number of class labels.