Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
Table 3: Results of 5-fold cross-validation for aspect-specific sentiment extraction using different feature combinations.
['Features', 'Accuracy']
To evaluate the aspect-specific sentiment extraction, we extract polarity labels for all aspect terms of the ground truth annotations. By separating aspect term extraction and sentiment extraction, we can better evaluate the sentiment extraction in isolation. Again, we only consider unique aspect terms that are either labeled with a positive or negative polarity. We report the performance of our sentiment extraction in terms of the accuracy of the system for different feature combinations. WE, WE-Retro, POS and Sentics are defined as before, while Dist denotes the obligatory distance embedding features. \newfloatcommandcapbtabboxtable[][0.48] While the retrofitted embeddings do not contribute positively to the performance for sentiment extraction either, a notable gain is achieved using the sentic vectors in our component for aspect-specific sentiment extraction. Here, we observe a gain of 3.5% points accuracy compared to using only word embeddings, distance embeddings and POS tags. Apart from that, the usage of sentic vectors drastically reduces the training time needed to achieve these results. The best results for the WE+POS+Dist and WE-Retro+POS+Dist model were achieved with 102 iterations over the training portion of the data, while the WE+POS+Dist+Sentic and WE-Retro+POS+Dist+Sentic model reached their best performances for only 12 and 9 iterations, respectively.