Aspect-level Cross-linguistic Multi-layer Sentiment Analysis: A Case Study on User Preferences for Mask Attributes During the COVID-19 Pandemic

Abstract

Amidst the COVID-19 pandemic, medical protective masks emerged as essential protective gear for the public. This paper aims to construct a nuanced, portable aspect-level sentiment analysis method, designed to unearth insightful information about attitudes toward such masks. The method is built upon three pivotal functional layers: sentiment intensity prediction, classification, and sentiment score calculation, collaboratively revealing consumer sentiments. For predicting sentiment intensity, we employ the Locally Weighted Linear Regression (LWLR) method, enhancing the Chinese VA sentiment lexicon while considering elements like foreign culture and value variations. Additionally, a context-adaptive modifier learning model adjusts word sentiment intensity. Sentiment classification leverages a dynamic XLNet mechanism and utilizes a Bi-LSTM model with stacked residuals for precise results. The sentiment score is astutely calculated by amalgamating sentiment classification and intensity prediction outcomes through the economically-recognized SRC index method. Through a case study using “User Preferences for Mask Attributes” as an example, the method demonstrated exceptional performance across numerous evaluation metrics. Furthermore, a qualitative analysis of the data elucidates the rationale behind varied sentiments concerning medical protective masks and epidemic prevention products.