@inproceedings{pereg-etal-2019-absapp,
title = "{ABSA}pp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System",
author = "Pereg, Oren and
Korat, Daniel and
Wasserblat, Moshe and
Mamou, Jonathan and
Dagan, Ido",
editor = "Pad{\'o}, Sebastian and
Huang, Ruihong",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-3001/",
doi = "10.18653/v1/D19-3001",
pages = "1--6",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
%A Pereg, Oren
%A Korat, Daniel
%A Wasserblat, Moshe
%A Mamou, Jonathan
%A Dagan, Ido
%Y Padó, Sebastian
%Y Huang, Ruihong
%S 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
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F pereg-etal-2019-absapp
%X 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.
%R 10.18653/v1/D19-3001
%U https://aclanthology.org/D19-3001/
%U https://doi.org/10.18653/v1/D19-3001
%P 1-6
Markdown (Informal)
[ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System](https://aclanthology.org/D19-3001/) (Pereg et al., EMNLP-IJCNLP 2019)
ACL
- Oren Pereg, Daniel Korat, Moshe Wasserblat, Jonathan Mamou, and Ido Dagan. 2019. ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System. In 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, pages 1–6, Hong Kong, China. Association for Computational Linguistics.