Active learning in annotating micro-blogs dealing with e-reputation
Table 3: Numbers of corrections for each candidate and each task.
['Correction Type Polarity', '# of correction Hollande', '# of correction Sarkozy']
Although NS only possess 17% additional raw annotations regarding FH, it concentrates much more corrections regarding the opinions while conversely the trend is reversed with respect to the aspects. We can mainly explain this with the label distribution since the positive classes are not really existing with FH, it lower the task’s complexity. ML and content-based approaches did not help much to improve the annotation-correction process for the opinion detection issue while profile statistics appeared to play a key role. In addition, it is interesting to notice that for NS even after a committee statement it was still impossible to agree on a label for some messages which were finally rejected. Finally in many cases regarding aspects, neither rules, ML approach nor the committee were able to agree and an additional referee was asked to provide a supplementary annotation.