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- W4211147081 abstract "One popular task of textual data is sentiment analysis. From machine learning point of view, the problem of sentiment analysis is to classify textual data into a class of sentiment such as positive, negative, or neutral. Convolutional Neural Network (CNN) is one of deep learning models that gives good results for some textual data tasks including sentiment analysis. A newer deep learning architecture which is a combination of Long-Short Term Memory and Convolutional Neural Network (LSTM-CNN) shows better results for sentiment analysis. In this paper, we analyzed the performance of CNN and LSTM-CNN for lifelong learning of sentiment analysis on Indonesian social media. After sequentially learning from several domains, our simulations show that LSTM-CNN is better at increasing the ability to transfer their knowledge to new domains when compared to CNN. However, LSTM-CNN losses more knowledge than CNN on a current domain." @default.
- W4211147081 created "2022-02-13" @default.
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- W4211147081 date "2022-01-01" @default.
- W4211147081 modified "2023-10-18" @default.
- W4211147081 title "Analysis of Performance Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) for Lifelong Learning on Indonesian Sentiment Analysis" @default.
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- W4211147081 doi "https://doi.org/10.1007/978-3-030-90639-9_80" @default.
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