[1Win4-76] Development of GPT-LSTM based Sentiment Interpretable Neural Network and Application to the Financial Sentiment Adaptation
Keywords:Sentiment Analysis, XAI
When deploying deep neural networks (DNNs) to services related to the financial area, "computational cost" and "black-box nature of updated parameters in DNNs" can be critical issues.
To solve this problem, we first propose a novel sentiment interpretable neural network called GPT-LSTM based Sentiment Interpretable Neural Network (GL-SINN).
In addition, as an apllication of this study, we propose a domain word polarity conversion method called "Word-level Polarity Adaptation framework based on SINN (WPAS)", which is the method of sentiment domain adaptation in a cost effectibve manner.
To solve this problem, we first propose a novel sentiment interpretable neural network called GPT-LSTM based Sentiment Interpretable Neural Network (GL-SINN).
In addition, as an apllication of this study, we propose a domain word polarity conversion method called "Word-level Polarity Adaptation framework based on SINN (WPAS)", which is the method of sentiment domain adaptation in a cost effectibve manner.
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