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aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 219-234页

作者： Rakesh Dutta Nilanjana Das Mukta Majumder Biswapati Jana Department of Computer Science and Application Hijli CollegeKharagpurIndia WBSEDCL Midnapore ZoneMidnaporeWest BengalIndia Department of Computer Science and Application University of North BengalSiliguriIndia Department of Computer Science Vidyasagar UniversityMidnaporeWest BengalIndia

The COVID-19 pandemic has a significant impact on the global economy and *** the pandemic continues to cause casualties in millions,many countries have gone under *** this period,people have to stay within walls and become more addicted towards social *** express their emotions and sympathy via these online ***,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and sentiment analysis on COVID-19-related *** have used aspect based sentiment analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock *** goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of ***-specific COVID-19 tweets have been annotated,for analysing the sentiment of common *** classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data *** suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

关键词： aspect based sentiment analysis bi-directional gated recurrent unit COVID-19 deep learning k-means clustering multi-criteria decision-making natural language processing

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Graph Enhanced Transformer for aspect Category Detection

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Journal of Computer Science & Technology 2023年第3期38卷 612-625页

作者：陈晨王厚峰朱晴晴柳军飞 Office of the Cyberspace Affairs Commission Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China School of Software and Microelectronics Peking UniversityBeijing 100871China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.

关键词： aspect based sentiment analysis aspect category detection hierarchical multi-label classification transformer network

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic sentiment Classification

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Computer Systems Science & Engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

aspect-based sentiment analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained sentiment analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic aspect based sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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