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arXiv · 1802.05818

Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning

Shuai Wang·Mianwei Zhou·Sahisnu Mazumder·Bing Liu·Yi Chang
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Abstract

Given a target name, which can be a product aspect or entity, identifying its aspect words and opinion words in a given corpus is a fine-grained task in target-based sentiment analysis (TSA). This task is challenging, especially when we have no labeled data and we want to perform it for any given domain. To address it, we propose a general two-stage approach. Stage one extracts/groups the target-related words (call t-words) for a given target. This is relatively easy as we can apply an existing semantics-based learning technique. Stage two separates the aspect and opinion words from the grouped t-words, which is challenging because we often do not have enough word-level aspect and opinion labels. In this work, we formulate this problem in a PU learning setting and incorporate the idea of lifelong learning to solve it. Experimental results show the effectiveness of our approach.

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Shuai Wang, Mianwei Zhou, Sahisnu Mazumder, Bing Liu, Yi Chang. 2018-02-16. Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning. https://arxiv.org/abs/1802.05818

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