A Simple Approach to Multilingual Polarity Classification in Twitter
Table 4: B4MSA’s performance on cross-validation and gold standard. The subscript at right of each score means for the random-search’s parameter (sampling size) needed to find that value.
['Dataset', '[EMPTY]', 'Multilingual Parameters Random Search', 'Multilingual Parameters Random Search', 'Multilingual Parameters H+M Search', 'Multilingual Parameters H+M Search', 'Language-Dependent Parameters Random Search', 'Language-Dependent Parameters Random Search', 'Language-Dependent Parameters H+M Search', 'Language-Dependent Parameters H+M Search']
The reached performance is reported using both cross-validation and the official gold-standard. Please notice how H+M consistently reaches better performances, even on small sampling sizes. Note that, in SemEval challenges, the cross-validation performances are higher than those reached by evaluating the gold-standard, mainly because the gold-standard does not follow the distribution of training set. This can be understood because the rules of SemEval promote the use of external knowledge.