Causal Intervention Improves Implicit Sentiment Analysis
This work addresses the robustness and effectiveness issues in implicit sentiment analysis, which is crucial for applications like social media monitoring and customer feedback analysis, though it is incremental as it builds on causal methods for a known bottleneck.
The authors tackled the problem of implicit sentiment analysis, where existing neural models often rely on spurious correlations like explicit sentiment words, by proposing a causal intervention model using instrumental variables to eliminate confounding effects and extract pure causal relationships, achieving significant advantages over strong baselines in both general and aspect-based tasks.
Despite having achieved great success for sentiment analysis, existing neural models struggle with implicit sentiment analysis. This may be due to the fact that they may latch onto spurious correlations ("shortcuts", e.g., focusing only on explicit sentiment words), resulting in undermining the effectiveness and robustness of the learned model. In this work, we propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV). We first review sentiment analysis from a causal perspective and analyze the confounders existing in this task. Then, we introduce an instrumental variable to eliminate the confounding causal effects, thus extracting the pure causal effect between sentence and sentiment. We compare the proposed ISAIV model with several strong baselines on both the general implicit sentiment analysis and aspect-based implicit sentiment analysis tasks. The results indicate the great advantages of our model and the efficacy of implicit sentiment reasoning.