BERT-based multi-task learning for aspect-based sentiment analysis
Date
2022-01-20
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Abstract
The Aspect Based Sentiment Analysis (ABSA) systems aims to extract the aspect terms
(e.g., pizza, staff member), Opinion terms (e.g., good, delicious), and their polarities (e.g.,
Positive, Negative, and Neutral), which can help the customers and companies to identify
product weaknesses. By solving these product weaknesses, companies can enhance
customer satisfaction, increase sales, and boost revenues. There are several approaches to
perform the ABSA tasks, such as classification, clustering, and association rule mining. In
this research we have used a neural network-based classification approach. The most
prominent neural network-based methods to perform ABSA tasks include BERT-based
approaches, such as BERT-PT and BAT. These approaches build separate models to
complete each ABSA subtasks, such as aspect term extraction (e.g., pizza, staff member)
and aspect sentiment classification. Furthermore, both approaches use different training
algorithms, such as Post-Training and Adversarial Training. Moreover, they do not
consider the subtask of Opinion Term Extraction.
This thesis proposes a new system for ABSA, called BERT-ABSA, which uses MultiTask Learning (MTL) approach and differentiates from these previous approaches by
solving all three tasks such as aspect terms, opinion terms extraction, and aspect term
related sentiment detection simultaneously by taking advantage of similarities between
tasks and enhancing the model’s accuracy as well as reduce the training time. To evaluate
our model’s performance, we have used the SemEval-14 task 4 restaurant datasets. Our
model outperforms previous models in several ABOM tasks, and the experimental results
support its validity
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Keywords
Aspect-based opinion mining, Product aspect and opinion extraction, Multi-task learning, BERT