Distance Metric Learning for Aspect Phrase Grouping
This work addresses aspect-level sentiment analysis for review analysis, but it appears incremental as it builds on existing metric learning and attention mechanisms.
The paper tackled the problem of aspect phrase grouping in sentiment analysis by proposing an Attention-based Deep Distance Metric Learning (ADDML) method, which outperformed state-of-the-art baselines on four review datasets.
Aspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by considering aspect phrase representation as well as context representation. First, leveraging the characteristics of the review text, we automatically generate aspect phrase sample pairs for distant supervision. Second, we feed word embeddings of aspect phrases and their contexts into an attention-based neural network to learn feature representation of contexts. Both aspect phrase embedding and context embedding are used to learn a deep feature subspace for measure the distances between aspect phrases for K-means clustering. Experiments on four review datasets show that the proposed method outperforms state-of-the-art strong baseline methods.