ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System

Oren Pereg, Daniel Korat, Moshe Wasserblat, Jonathan Mamou, Ido Dagan


Abstract
We present ABSApp, a portable system for weakly-supervised aspect-based sentiment ex- traction. 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.
Anthology ID:
D19-3001
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://www.aclweb.org/anthology/D19-3001
DOI:
10.18653/v1/D19-3001
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-3001.pdf