Relational Graph Attention Network for Aspect-based Sentiment Analysis
Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT.
['Category', '(%)', 'Example']
, we randomly select 100 misclassified examples by two models (R-GAT and R-GAT+BERT) from the Restaurant dataset. After looking into these bad cases, we find the reasons behind can be classified into four categories. The second category is due to the difficulty in comprehension, which may demand deep language understanding techniques such as natural language inference. The third category is caused by the advice which only recommend or disrecommend people to try, with no obvious clues in the sentences indicating the sentiments. The fourth category is caused by double negation expression, which is also difficult for current models. Through the error analysis, we can note that although current models have achieved appealing progress, there are still some complicated sentences beyond their capabilities. There ought to be more advanced natural language processing techniques and learning algorithms developed to further address them.