Understanding patient reviews with minimum supervision.

Area of Science:

• Natural Language Processing
• Health Informatics
• Machine Learning

Background:

• Online patient reviews offer valuable insights into healthcare service quality.
• Extracting specific opinions and their targets from these reviews is crucial for service improvement.
• Existing aspect-based sentiment analysis (ABSA) methods struggle due to a lack of aspect-level annotations.

Purpose of the Study:

• To develop a joint learning framework for unsupervised aspect extraction and supervised sentiment classification.
• To address the challenge of limited aspect-level annotations in healthcare reviews.
• To enable timely identification and response to patient concerns regarding health services.

Main Methods:

• A novel joint learning framework was proposed.
• The framework simultaneously performs unsupervised aspect extraction at the sentence level.
• It also conducts supervised sentiment classification at the document level.

Main Results:

• Achieved 98.2% accuracy in sentiment classification on Yelp healthcare reviews.
• Outperformed several strong baseline models in sentiment analysis.
• Successfully extracted coherent aspects and inferred their polarity distribution without aspect-level annotations.

Conclusions:

• The proposed joint learning framework effectively analyzes patient opinions in healthcare reviews.
• The model demonstrates high accuracy in sentiment classification and aspect extraction.
• This approach offers a viable solution for understanding patient feedback without requiring extensive manual annotation.