Transfer Learning With Document-Level Data Augmentation for Aspect-Level Sentiment Classification

doi:10.1109/TBDATA.2023.3310267

Transfer Learning With Document-Level Data Augmentation for Aspect-Level Sentiment Classification

2023-12-01

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DOI：10.1109/TBDATA.2023.3310267

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Aspect-level sentiment classification (ASC) seeks to reveal the emotional tendency of a designated aspect of a text. Some researchers have recently tried to exploit large amounts of document-level sentiment classification (DSC) data available to help improve the performance of ASC models through transfer learning. However, these studies often ignore the difference in sentiment distribution between document-level and aspect-level data without preprocessing the document-level knowledge. Our study provides a transfer learning with document-level data augmentation (TL-DDA) framework to transfer more accurate document-level knowledge to the ASC model by means of document-level data augmentation and attention fusion. First, we use document data selection and text concatenation to produce document-level data with various sentiment distributions. The augmented document data is then utilized for pre-training a well-designed DSC model. Finally, after attention adjustment, we fuse the word attention obtained from this DSC model into the ASC model. Results of experiments utilizing two publicly available datasets suggest that TL-DDA is reliable.

Keywords:

Task analysis

Data models

Training

Feature extraction

Transfer learning

Data augmentation

Big Data

document-level data augmentation

aspect-level sentiment classification

Transfer Learning With Document-Level Data Augmentation for Aspect-Level Sentiment Classification

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