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• W2765664019 abstract "In this study, we investigate various deep learning models based on convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) recurrent neural networks for sentiment analysis of Arabic microblogs. Unlike English, the Arabic language has several specifics which complicate the process of feature extraction by traditional methods. We adopted a neural language model created at Google, known as word2vec, for vectorizing text. We then designed and evaluated several deep learning architectures using CNN and LSTM. The experiments were run on two publicly available Arabic tweets datasets. Promising results have been attained when combining LSTMs and compared favorably with most related work." @default.
• W2765664019 created "2017-11-10" @default.
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• W2765664019 date "2017-01-01" @default.
• W2765664019 modified "2023-10-16" @default.
• W2765664019 title "Hybrid Deep Learning for Sentiment Polarity Determination of Arabic Microblogs" @default.
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• W2765664019 doi "https://doi.org/10.1007/978-3-319-70096-0_51" @default.
• W2765664019 hasPublicationYear "2017" @default.
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