Representation of texts as complex networks: a mesoscopic approach
Table 2: Comparison of the K-means clustering performance among different network approaches. Two different measurements were applied: Adjusted Rand Index (ARI) and Accuracy. In both measurements, 1 indicates that all instances are correctly classified and 0 indicates the opposite.
['[EMPTY]', 'ARI', 'Accuracy']
The unsupervised approach was also used to compare the proposed methodology and traditional co-occurrence networks. In this analysis, we used 10 principal components, as this amount of features yielded optimized results. In this scenario, 72.5% of SP texts were incorrectly classified as RT. This inability is also reflected in the ARI, which is much lower in co-occurrence networks.