[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121986-en":3,"doc-seo-121986-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121986,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification - Article","Sentiment analysis is central to natural language processing, yet relying only on overall sentiment fails to capture fine-grained emotional cues. This work targets multi-label aspect extraction, where different emotional aspects must be predicted from review text. The proposed Ensemble of DenseNet based on Aquila Optimizer (EDAO) prioritizes both diversity and accuracy in multi-label learners. Experiments on seven datasets—emotions, hotels, movies, proteins, automobiles, medical, and news—combine preprocessing, Vader with Bag of Words for feature extraction, word2vec-based associations, and DenseNet fine-tuned with Aquila Optimizer. Results on aspect-based multi-labeling show up to 95–97% accuracy and demonstrate consistent gains over standard benchmarks.","algorithms  \nArticle  \nAn Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification  \nNasir Ayub 1, *, Tayyaba 2, Saddam Hussain 3, Syed Sajid Ullah 4, * and Jawaid Iqbal 5  \nCitation: Ayub, N.; Tayyaba; Hussain, S.; Ullah, S.S.; Iqbal, J. An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification. Algorithms 2023, 16, 548. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)a16120548  \nAcademic Editor: Jesper Jansson  \nReceived: 4 October 2023  \nRevised: 21 November 2023  \nAccepted: 23 November 2023  \nPublished: 28 November 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Creative Technologies, Air University Islamabad, Islamabad 44000, Pakistan  \n2 Department of Computing, Riphah International University, Faisalabad 38000, Pakistan  \n3 School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei; [saddamicup1993@gmail.com](saddamicup1993@gmail.com)  \n4 Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway  \n5 Faculty of Computing, Riphah International University, Islamabad 44000, Pakistan  \n* [Correspondence: nasir.ayub@mail.au.edu.pk](Correspondence: nasir.ayub@mail.au.edu.pk) (N.A.); [syed.s.ullah@uia.no](syed.s.ullah@uia.no) (S.S.U.)  \nAbstract: Sentiment analysis holds great importance within the domain of natural language processing as it examines both the expressed and underlying emotions conveyed through review content. Furthermore, researchers have discovered that relying solely on the overall sentiment derived from the textual content is inadequate. Consequently, sentiment analysis was developed to extract nuanced expressions from textual information. One of the challenges in this field is effectively extracting emotional elements using multi-label data that covers various aspects. This article presents a novel approach called the Ensemble of DenseNet based on Aquila Optimizer (EDAO). EDAO is specifically designed to enhance the precision and diversity of multi-label learners. Unlike traditional multi-label methods, EDAO strongly emphasizes improving model diversity and accuracy in multi-label scenarios. To evaluate the effectiveness of our approach, we conducted experiments on seven distinct datasets, including emotions, hotels, movies, proteins, automobiles, medical, news, and birds. Our initial strategy involves establishing a preprocessing mechanism to obtain precise and refined data. Subsequently, we used the Vader tool with Bag of Words (BoW) for feature extraction. In the third stage, we created word associations using the word2vec method. The improved data were also used to train and test the DenseNet model, which was fine-tuned using the Aquila Optimizer (AO) . On the news, emotion, auto, bird, movie, hotel, protein, and medical datasets, utilizing the aspectbased multi-labeling technique, we achieved accuracy rates of 95%, 97%, and 96%, respectively, with DenseNet-AO. Our proposed model demonstrates that EDAO outperforms other standard methods across various multi-label datasets with different dimensions. The implemented strategy has been rigorously validated through experimental results, showcasing its effectiveness compared to existing benchmark approaches.  \nKeywords: classification; multi-labeling; natural language processing; deep learning; optimization method; sentiment analysis  \n1. Introduction  \nSentiment analysis, also known as opinion mining, has become an increasingly important topic of discussion in various fields. It is crucial in evaluating customer feedback, understanding public opinions, and tracking real-world ","cbCaihkPUdA7Bt7P","https://ap.wps.com/l/cbCaihkPUdA7Bt7P","pdf",1187128,1,30,"English","en",105,"# Introduction\n## Sentiment analysis and opinion mining\n## Multi-label vs multi-class sentiment tasks\n# Proposed approach (EDAO)","[{\"question\":\"Why is aspect-based multi-label sentiment analysis important compared with overall sentiment?\",\"answer\":\"Overall sentiment summarizes text but misses nuanced emotional elements. Aspect-based multi-labeling extracts different emotional aspects covered by the review content.\"},{\"question\":\"What is EDAO and how does it improve multi-label learning?\",\"answer\":\"EDAO is an Ensemble of DenseNet based on Aquila Optimizer, designed to enhance both precision and diversity of multi-label learners rather than using only traditional multi-label strategies.\"},{\"question\":\"How was the proposed model evaluated and what datasets were used?\",\"answer\":\"Experiments were conducted on seven datasets, including emotions, hotels, movies, proteins, automobiles, medical, and news, using aspect-based multi-label labeling with DenseNet fine-tuned by Aquila Optimizer.\"}]","An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification - Article | PDF",1785808162,76,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-efficient-optimized-densenet-model-for-aspect-based-multi-label-classification-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-efficient-optimized-densenet-model-for-aspect-based-multi-label-classification-article/121986/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is aspect-based multi-label sentiment analysis important compared with overall sentiment?","Question",{"text":75,"@type":76},"Overall sentiment summarizes text but misses nuanced emotional elements. Aspect-based multi-labeling extracts different emotional aspects covered by the review content.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is EDAO and how does it improve multi-label learning?",{"text":80,"@type":76},"EDAO is an Ensemble of DenseNet based on Aquila Optimizer, designed to enhance both precision and diversity of multi-label learners rather than using only traditional multi-label strategies.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the proposed model evaluated and what datasets were used?",{"text":84,"@type":76},"Experiments were conducted on seven datasets, including emotions, hotels, movies, proteins, automobiles, medical, and news, using aspect-based multi-label labeling with DenseNet fine-tuned by Aquila Optimizer.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]