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- W2883660486 abstract "For the artificial intelligence (AI) to effectively mimic humans, understanding humans, more specifically, human emotion is important. Sentiment analysis aims to automatically uncover the underlying sentiment or emotions that humans hold towards an entity. There is high ambiguity of emotion in text data. In this paper, we consider the sentence-level sentiment classification task, and propose a novel type of convolutional neural network combined with fuzzy logic called the Fuzzy Convolutional Neural Network (FCNN) and its associated learning algorithm. The new model is an integration of modified Convolutional Neural Network (CNN) in the fuzzy logic domain. The proposed model benefits from the use of fuzzy membership degrees to produce more refined outputs, thereby reducing the ambiguities in emotional aspects of sentiment classification. Also it benefits from extracting high-level emotional features due to convolutional neural representation. We compare the performance of our proposed approach with conventional CNN for sentiment classification. The experimental results indicate that the proposed FCNN outperforms the conventional methods for sentiment classification task." @default.
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- W2883660486 date "2018-12-24" @default.
- W2883660486 modified "2023-10-03" @default.
- W2883660486 title "A fuzzy convolutional neural network for text sentiment analysis" @default.
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- W2883660486 doi "https://doi.org/10.3233/jifs-169843" @default.
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