Abstract
Aspect-Based Sentiment Classification (ABSC) models are increasingly utilised given the surge in opinionated text displayed on the Web. This paper aims to explain the outcome of a black box state-of-the-art deep learning model used for ABSC, LCR-Rot-hop++. We compare two sampling methods that feed an interpretability algorithm which is based on local linear approximations (LIME). One of the sampling methods, SS, swaps out different words from the original sentence with other similar words to create neighbours to the original sentence. The second method, SSb, uses SS and then filters its neighbourhood to better balance the sentiment proportions in the localities created. We use a 2016 restaurant reviews dataset for ternary classification and we judge the interpretability algorithms based on their hit rate and fidelity. We find that SSb can improve neighbourhood sentiment balance compared to SS, reducing bias for the majority class, while simultaneously increasing the performance of LIME.
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Miron, V., Frasincar, F., Truşcǎ, M.M. (2023). Explaining a Deep Learning Model for Aspect-Based Sentiment Classification Using Post-hoc Local Classifiers. In: Métais, E., Meziane, F., Sugumaran, V., Manning, W., Reiff-Marganiec, S. (eds) Natural Language Processing and Information Systems. NLDB 2023. Lecture Notes in Computer Science, vol 13913. Springer, Cham. https://doi.org/10.1007/978-3-031-35320-8_6
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