ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
This system addresses the need for cost-effective and rapid sentiment analysis across different domains, though it appears incremental as it builds on existing weakly-supervised methods.
The authors tackled the problem of aspect-based sentiment extraction without labeled data by developing ABSApp, a portable weakly-supervised system that generates domain-specific lexicons and allows user editing, resulting in successful application in real-life use cases like movie review analysis.
We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction. The system is interpretable and user friendly and does not require labeled training data, hence can be rapidly and cost-effectively used across different domains in applied setups. The system flow includes three stages: First, it generates domain-specific aspect and opinion lexicons based on an unlabeled dataset; second, it enables the user to view and edit those lexicons (weak supervision); and finally, it enables the user to select an unlabeled target dataset from the same domain, classify it, and generate an aspect-based sentiment report. ABSApp has been successfully used in a number of real-life use cases, among them movie review analysis and convention impact analysis.