Using Hadoop for Large Scale Analysis on Twitter: A Technical Report
Table 1: Classification results for emoticons and hashtags (BF stands for Bloom filter and NBF for no Bloom filter)
['Setup', 'BF', 'NBF', 'Random baseline']
In this subsection we measure the classification performance of our solution using the harmonic f-score. We use two experimental settings, the multi-class classification and the binary classification settings. Under multi-class classification we attempt to assign a single label to each of vectors in the test set. In the binary classification experiments, we classified a sentence as either appropriate for a particular label or as not bearing any sentiment. We also test how the performance is affected with and without using Bloom filters. The value k for the kNN classifier is equal to 50. In case of binary classification, the results depict the average score for all classes.