Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
This addresses aspect extraction for fine-grained sentiment analysis in product reviews, offering a simple yet effective approach that outperforms more complex methods.
The paper tackled aspect extraction in product reviews by proposing a CNN model that uses both general-purpose and domain-specific pre-trained embeddings, achieving state-of-the-art results without additional supervision.
One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.