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.
@article{arxiv.1909.05608,
title = {ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System},
author = {Oren Pereg and Daniel Korat and Moshe Wasserblat and Jonathan Mamou and Ido Dagan},
journal= {arXiv preprint arXiv:1909.05608},
year = {2019}
}