| Experiments | These results highlight a special property of sentence-level annotation: greater sensitivity to sparseness of the model: On texts, classifier error on one particular sentiment marker is often compensated by a number of correctly identified other sentiment clues. |
| Experiments | Since sentences usually contain a much smaller number of sentiment clues than texts, sentence-level annotation more readily yields errors when a single sentiment clue is incorrectly identified or missed by the system. |
| Experiments | training sets are required to overcome this higher n-gram sparseness in sentence-level annotation. |
| Factors Affecting System Performance | To our knowledge, the only work that describes the application of statistical classifiers (SVM) to sentence-level sentiment classification is (Gamon and Aue, 2005)1. |