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Open AccessArticle

A CNN-BiLSTM Model for Document-Level Sentiment Analysis

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IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, Rabat 10100, Morocco
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Meridian Team, LYRICA Laboratory, School of Information Sciences, Rabat 10100, Morocco
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LRIT Laboratory, Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat 10100, Morocco
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SIP Research Team, Rabat IT Center, EMI, Mohammed V University in Rabat, Rabat 10100, Morocco
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Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(3), 832-847; https://doi.org/10.3390/make1030048
Received: 30 June 2019 / Revised: 22 July 2019 / Accepted: 23 July 2019 / Published: 25 July 2019
Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing press articles and blog posts about a particular product or company, and it requires a high concentration, especially when the topic being discussed is sensitive. Nevertheless, most existing models and techniques are designed to process short text from social networks and collaborative platforms. In this paper, we propose a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models, with Doc2vec embedding, suitable for opinion analysis in long texts. The CNN-BiLSTM model is compared with CNN, LSTM, BiLSTM and CNN-LSTM models with Word2vec/Doc2vec embeddings. The Doc2vec with CNN-BiLSTM model was applied on French newspapers articles and outperformed the other models with 90.66% accuracy. View Full-Text
Keywords: sentiment analysis; document level; Doc2vec; CNN-BiLSTM sentiment analysis; document level; Doc2vec; CNN-BiLSTM
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Rhanoui, M.; Mikram, M.; Yousfi, S.; Barzali, S. A CNN-BiLSTM Model for Document-Level Sentiment Analysis. Mach. Learn. Knowl. Extr. 2019, 1, 832-847.

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