SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering
Table 6. Confusion matrices obtained by SEntiMoji on the Java Library dataset.
['[EMPTY]', '[EMPTY]', 'Predicted Polarity positive', 'Predicted Polarity neutral', 'Predicted Polarity negative']
In terms of the performance of SentiCR, nearly half of the sentences classified as positive or negative are actually false (precision@pos: 0.553 , precision@neg: 0.546), which is far from being qualified for evaluating a software library. The precision levels of other existing methods are even lower. By comparison, SEntiMoji can achieve a comparable recall level but significantly improve precision results over positive and negative samples (precision@pos: 0.849, precision@neg: 0.729). It is promising for the aforementioned recommendation task.