Performance analysis of aspect-level sentiment classification task based on different deep learning models.

ABSTRACT: Aspect-level sentiment classification task (ASCT) is a natural language processing task that aims to correctly identify specific aspects and determine their sentiment polarity from a given target sentence. Deep learning models have been proven to be effective in aspect-based sentiment classification tasks, and the mainstream Aspect-level sentiment classification (ASC) models currently constructed generally assume that the training and test datasets are Gaussian distribution (e.g., the same language). Once the data distribution changes, the ASC model must be retrained on the new distribution data to achieve good performance. However, acquiring a large amount of labeled data again typically requires a lot of manpower and money, which seems unlikely, especially for the ASC task, as it require

SUBMITTER: Cao F 

PROVIDER: S-EPMC10588683 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature