Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning
This addresses a fine-grained challenge in target-based sentiment analysis for domains lacking labeled data, but it is incremental as it builds on existing semantics-based techniques and PU learning.
The paper tackled the problem of identifying aspect and opinion words for a given target in sentiment analysis without labeled data, proposing a two-stage approach using PU learning and lifelong learning, with experimental results showing its effectiveness.
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.