Using Hadoop for Large Scale Analysis on Twitter: A Technical Report
Table 2: Fraction of tweets with no matching vectors
['Setup', 'BF', 'NBF']
For multi-class classification the results are not very good but still they are way above the random baseline. We also observe that the results with and without the Bloom filters are almost the same. Thus, we deduce that for multi-class classification the Bloom filters marginally affect the classification performance. Furthermore, the outcome for emoticons is significantly better than hashtags which is expected due to the lower number of sentiment types. This behavior can also be explained by the ambiguity of hashtags and some overlap of sentiments. In case of binary classification there is a notable difference between the results with and without Bloom filters. w (no matching vectors are found). Notice that the integration of Bloom filters, leads to a bigger number of tweets with no matching vectors. Obviously, the excluded tweets have an immediate effect to the performance of the kNN classifier in case of binary classification. This happens since the number of tweets in the cross fold validation process is noticeably smaller compared to the multi-class classification. Overall, the results for binary classification with Bloom filters confirm the usefulness of our approach.