{"title":"Arabic Sentiment Analysis based on Deep Reinforcement Learning","authors":"Mohamed Zouidine, Mohammed Khalil","doi":"10.1109/NISS55057.2022.10085147","DOIUrl":null,"url":null,"abstract":"In this work, we handle the problem of Arabic sentiment analysis by combining the Arabic language understanding transformer-based model AraBERT and an LSTM-CNN deep learning model. We propose a new training objective function based on deep reinforcement learning that combines cross-entropy loss from maximum likelihood estimation and rewards from policy gradient algorithm. We evaluate our proposed system on the LABR book reviews dataset. Experimental results show that the proposed model outperforms the state-of-the-art models and provides an accuracy of 87.58%.","PeriodicalId":138637,"journal":{"name":"2022 5th International Conference on Networking, Information Systems and Security: Envisage Intelligent Systems in 5g//6G-based Interconnected Digital Worlds (NISS)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th International Conference on Networking, Information Systems and Security: Envisage Intelligent Systems in 5g//6G-based Interconnected Digital Worlds (NISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NISS55057.2022.10085147","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, we handle the problem of Arabic sentiment analysis by combining the Arabic language understanding transformer-based model AraBERT and an LSTM-CNN deep learning model. We propose a new training objective function based on deep reinforcement learning that combines cross-entropy loss from maximum likelihood estimation and rewards from policy gradient algorithm. We evaluate our proposed system on the LABR book reviews dataset. Experimental results show that the proposed model outperforms the state-of-the-art models and provides an accuracy of 87.58%.