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There is a need to know up to what degree humans can agree when classifying a sentence as carrying some sentiment orientation. However, a little research has been done on assessing the agreement between annotators for the different opinion mining tasks. In this work we present an assessment of agreement between two human annotators. The task was to manually classify newspaper sentences into one of three classes. For assessing the level of agreement, Cohen’s kappa coefficient was computed. Results show that annotators agree more for negative classes than for positive or neutral. We observed that annotators might agree up to a level of substantial agreement of 0.65 for the best case or 0.30 for the worst.

Let's Agree to Disagree: Measuring Agreement between Annotators for Opinion Mining Task