Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
Table 3: Impact of different components.
['Models', 'Restaurant Aspect', 'Restaurant Opinion', 'Laptop Aspect', 'Laptop Opinion']
To test the impact of each component of RNCRF and the three types of hand-crafted features, we conduct experiments on different model settings: DT-RNN+SoftMax: rather than using a CRF, a softmax classifier is used on top of DT-RNN. CRF+word2vec: a linear-chain CRF with word embeddings only without using DT-RNN. RNCRF+POS/NL/Lex: the RNCRF model with POS tag or name list or sentiment lexicon feature(s). Similarly, both aspect and opinion term labels are provided for training for each of the above models. Firstly, RNCRF achieves much better results compared to DT-RNN+SoftMax (+11.60% and +10.72% for the restaurant domain and the laptop domain in aspect extraction). This is because DT-RNN fails to fully exploit context information for sequence labeling, which, in contrast, can be achieved by CRF. Secondly, RNCRF outperforms CRF+word2vec, which proves the importance of DT-RNN for modeling interactions between aspects and opinions. Hence, the combination of DT-RNN and CRF inherits the advantages from both models. Moreover, by separately adding hand-crafted features, we can observe that name-list based features and the sentiment lexicon feature are most effective for aspect extraction and opinion extraction, respectively. This may be explained by the fact that name-list based features usually contain informative evident for aspect terms and sentiment lexicon provides explicit indication about opinions.