DA-BERT: Enhancing Part-of-Speech Tagging of Aspect Sentiment Analysis Using BERT
With the development of Internet, text-based data from web have grown exponentially where the data carry large amount of valuable information. As a vital branch of sentiment analysis, the aspect sentiment analysis of short text on social media has attracted interests of researchers. Aspect sentiment...
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Published in | Advanced Parallel Processing Technologies pp. 86 - 95 |
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Main Authors | , , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | With the development of Internet, text-based data from web have grown exponentially where the data carry large amount of valuable information. As a vital branch of sentiment analysis, the aspect sentiment analysis of short text on social media has attracted interests of researchers. Aspect sentiment classification is a kind of fine-grained textual sentiment classification. Currently, the attention mechanism is mainly combined with RNN (Recurrent Neural Network) or LSTM (Long Short-Term Memory) networks. Such neural network-based sentiment analysis model not only has a complicated computational structure, but also has computational dependence. To address the above problems and improve the accuracy of the target-based sentiment classification for short text, we propose a neural network model that combines deep-attention with Bidirectional Encoder Representations from Transformers (DA-BERT). The DA-BERT model can fully mine the relationships between target words and emotional words in a sentence, and it does not require syntactic analysis of sentences or external knowledge such as sentiment lexicon. The training speed of the proposed DA-BERT model has been greatly improved while removing the computational dependencies of RNN structure. Compared with LSTM, TD-LSTM, TC-LSTM, AT-LSTM, ATAE-LSTM, and PAT-LSTM, the results of experiments on the dataset SemEval2014 Task4 show that the accuracy of the DA-BERT model is improved by 13.63% on average where the word vector is 300 dimensions in aspect sentiment classification. |
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ISBN: | 9783030296100 3030296105 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-030-29611-7_7 |