Using Hadoop for Large Scale Analysis on Twitter: A Technical Report
Table 3: Effect of k in classification performance
['Setup', '[ITALIC] k=50', '[ITALIC] k=100', '[ITALIC] k=150', '[ITALIC] k=200']
In this subsection, we attempt to alleviate the problem of our approach’s low performance for binary classification without Bloom filters. To achieve this we measure the effect of k in the classification performance of the algorithm. We test four different configurations where k∈{50,100,150,200}. For both binary and multi-class classification, increasing k affects slightly (or not at all) the harmonic f-score when we embody Bloom filters. The same thing does not apply when we do not use Bloom filters. More specifically, there is a great enhancement in the binary classification performance for hashtags and emoticons and a smaller improvement in case of multi-class classification. The inference of this experiment, is that larger values of k can provide a great impulse in the performance of the algorithm when not using Bloom filters.