Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning
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.
Cite
@article{arxiv.1802.05818,
title = {Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning},
author = {Shuai Wang and Mianwei Zhou and Sahisnu Mazumder and Bing Liu and Yi Chang},
journal= {arXiv preprint arXiv:1802.05818},
year = {2018}
}