@inproceedings{a4b0b657a6974fb58c2ce50924093443,
title = "ABSApp: A portable weakly-supervised aspect-based sentiment extraction system",
abstract = "We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction 1. 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.",
author = "Oren Pereg and Daniel Korat and Moshe Wasserblat and Jonathan Mamou and Ido Dagan",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computational Linguistics.; 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 ; Conference date: 03-11-2019 Through 07-11-2019",
year = "2019",
language = "الإنجليزيّة",
series = "EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Proceedings of System Demonstrations",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1--6",
booktitle = "EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Proceedings of System Demonstrations",
address = "الولايات المتّحدة",
}