Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
Table 2: Results of 5-fold cross-validation for aspect term extraction using different feature combinations.
['Features', 'F1', 'Precision', 'Recall']
Since we do not have access to official evaluation scripts, we evaluate aspect term extraction using Precision, Recall and F1 score. Identical annotations i.e. annotations that target the same aspect term (in terms of character offsets) with the same polarity, are considered as one. Also, employing the sentic vectors for aspect term extraction degrades the networks performance. This is not completely unexpected, though, since the sentic vectors mainly encode sentiment information and aspect term extraction on its own is rather decoupled from the actual sentiment extraction. A more positive effect would be expected for the second step in our system, the prediction of polarity labels.