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.

ABSApp 是一个便于使用且可解释的便携式系统，用于弱监督的基于方面的情感提取，可快速且经济有效地用于不同领域的应用设置中，其流程包括三个阶段：基于未标记数据集生成特定于领域的方面和观点词典；使用户能够查看和编辑这些词典（弱监督）；使用户能够选择来自同一领域的未标记目标数据集，对其进行分类，并生成基于方面的情感报告。ABSApp 已经成功应用于大量真实生活案例，其中包括电影评论分析和会议影响分析。