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The Products. Warning: The seller(s) of this item that are POLISHED P.T. a towel or orchid house for your home, or to any get up with you (e.g., your mattress, up a laundry. Carpet cleaner solution, tools, jewelry, suit the purchase. to positive and negative words but overall each of them is represented twice in the matrix, so in the end each word is represented 6 times ([Fig. 1](#pone-0050900-g001){ref-type=”fig”}). This has an important consequence: Assuming the words each have equal probability of occurrence (i.e. 50%), the amount of ‚good’ and ‚bad’ words is exactly the same.

Now, if we translate this knowledge into the MDS model we found that the words ‚lose’,’success’ and ‚best’ form a cluster of the same dimension as ‚curse’ and ‚failure’. With low interword distances, we end up with a cluster with an almost identical composition to the words ‚curse’ and ‚failure’ and a cluster of ‚good’ and ‚bad’ words. This again stresses the fundamental similarity between RMA and the MDS model, but only for low interword distances. At higher distances, the resemblance between RMA and the MDS model disappears. The difference is an important one: Unlike the MDS model, RMA is based on pairwise distances derived from the contingency table. This means that the pairwise distances between all words are summed in order to get a single distance measure for the whole matrix. The MDS model, in contrast, is a non-parametric method, and a lot of information is integrated in the model. It can be said that RMA and the MDS model are both basing their conclusions on pairwise distances, but while RMA uses the information directly, the MDS model uses some of it after manipulation through non-metric multidimensional scaling.

The structural hole in the MDS model cannot be related to any ‚a priori’ number of clusters. It exists in any number of clusters, but the extent of the structural hole decreases when there are more clusters. This indicates that the existence of clusters of similar words is not caused by ‚a priori’ factors such as the dimensionality of the word-space but rather that there exists some latent meaning within the data which cannot be explained by the words. It could be that the

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