Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
Table 1: Tagging schema and relative position index, where B denotes begin, I denotes inside, E denotes end, S denotes single and O denotes outside. The check and cross marks denote valid and invalid aspect-opinion pairs.
['Input Unified tag (aspect+sentiment)', 'Input Unified tag (aspect+sentiment)', 'Waiters S-POS', 'are O', 'friendly O', 'and O', 'the O', 'fugu B-POS', 'sashimi E-POS', 'is O', 'out O', 'of O', 'the O', 'world O', '. O']
We propose a two-stage framework to address this task. In the first stage, we aim to extract potential aspect terms, together with their sentiment, and extract potential opinion terms. For the unified tagging system, it builds on top of two stacked Bidirectional Long Short Term Memory (BLSTM) networks. The upper one produces the aspect term and sentiment tagging results based on the unified tagging schema. The lower performs an auxiliary prediction of aspect boundaries with the aim for guiding the upper BLSTM. Gate mechanism is explicitly designed to maintain the sentiment consistency within each multi-word aspect. For the opinion term tagging system, it builds on top of a BLSTM layer and a Graph Convolutional Network (GCN) to make full use of semantic and syntactic information in a sentence. We specifically design a target-guiding module to transfer aspect information for opinion term extraction. After the first stage, we have obtained a bunch of aspects with sentiment polarities and a bunch of opinion expressions. In the second stage, the goal is to pair up aspects with the corresponding opinion expressions. Thus, we design distance embeddings to capture the distance between aspects and opinion expressions that are predicted from the stage one. With a BLSTM encoder, we encode sentence-level contexts into aspect and opinion terms for the final classification of candidate pairs. In the experiments, our framework has set a benchmark performance in this novel sentiment triplet extraction task. Meanwhile, our framework outperforms the state-of-the-art methods (with modification to fit in our task) and the strongest sequence taggers on several benchmark datasets. We also conduct extensive ablation tests to validate the rationality of our framework design. In our task formulation, however, we predict unified sequence tag term by term. It is possible, although contradictory, to have ‘fugu’ labeled as positive but ‘sashimi’ labeled as negative. gt⊙hSt+(1−gt)⊙~hSt−1 (1) where Wg and bg are model parameters of the SC module, and ⊙ is the element-wise multiplication. σ is a sigmoid function. With this gate mechanism, current time step prediction will also inherit features from the previous time step, reducing the risk of drastic sentiment label change.