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The framework bridges semantic representations of opinions across different linguistic formulations and is grounded in NLP opinion facets combined with symbolic descriptions. The paper defines the Unified Opinion Concept Extraction (UOCE) task, provides a manually extended and re-annotated evaluation dataset, and proposes tailored metrics to measure adherence to UOC semantics. Baselines are established using state-of-the-art generative models.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/towards-semantic-integration-of-opinions-unified-opinion-concepts-ontology-and-extraction-task/153537/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/towards-semantic-integration-of-opinions-unified-opinion-concepts-ontology-and-extraction-task/153537.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-08-28",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does the Unified Opinion Concepts (UOC) ontology aim to achieve?","Question",{"text":113,"@type":114},"It provides a unified conceptual framework to represent opinions semantically, consolidating opinion facets and enabling structured, unambiguous interpretation across formulations.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What is the Unified Opinion Concept Extraction (UOCE) task?",{"text":118,"@type":114},"UOCE is an NLP task focused on extracting opinions from text with enhanced expressivity, grounded in the rich semantic representation of the UOC ontology.",{"name":120,"@type":111,"acceptedAnswer":121},"How is the UOCE evaluation handled in the paper?",{"text":122,"@type":114},"The authors extend and re-annotate an existing opinion mining dataset and introduce tailored evaluation metrics that assess how well extracted opinions adhere to UOC semantics.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},153537,1787877828,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Towards Semantic Integration of Opinions: Unified Opinion Concepts  \nOntology and Extraction Task  \nGaurav Negi, Dhairya Dalal, Omnia Zayed, and Paul Buitelaar Insight SFI Research Centre for Data Analytics  \nData Science Institute  \nUniversity of Galway  \n{gaurav.negi, omnia.zayed, [paul.buitelaar}@insight-centre.org](paul.buitelaar}@insight-centre.org), [d.dalal1@universityofgalway.ie](d.dalal1@universityofgalway.ie)  \nAbstract  \nThis paper introduces the Unified Opinion Concepts (UOC) ontology to integrate opinions within their semantic context. The UOC ontology bridges the gap between the semantic representation of opinion across different formulations. It is a unified conceptualisation based on the facets of opinions studied extensively in NLP and semantic structures described through symbolic descriptions. We further propose the Unified Opinion Concept Extraction (UOCE) task of extracting opinions from the text with enhanced expressivity. Additionally, we provide a manually extended and re-annotated evaluation dataset for this task and tailored evaluation metrics to assess the adherence of extracted opinions to UOC semantics. Finally, we establish baseline performance for the UOCE task using state-of-the-art generative models.  \n1 Introduction  \nOpinion 1 mining has seen a move from a traditional sentence-and document-level analysis (Pang et al., 2002) to fine-grained approaches. Aspect-based Sentiment Analysis (ABSA) is a notable approach for fine-grained opinion mining, and it has been extensively studied in natural language processing (NLP) research. (Pontiki et al., 2014, 2015, 2016 ; Maia et al., 2018a) . The task focuses on identifying the aspects of the entities and their associated sentiments from a given text sequence. In the following sentence:  \n“I had hoped for better battery life , as it had only about 2-1/2 hours doing heavy computations (8 threads using 100 % of the CPU) . \"  \nABSA results are extracted as the following tuple:{battery life, Battery\\#Operational_Performance,  \n1We use the term opinion as a broad concept that covers sentiment and its associated information such as opinion target and the person who holds the opinion, and use the term sentiment to mean only the underlying positive, negative or neutral polarity implied by opinion.  \nnegative} . The extracted tuple is in the form {aspect term/opinion target, entity\\#aspect category, sentiment polarity} . Opinion target (often called aspect term) is the word or phrase over which an opinion is expressed. The aspect category is an attribute of the opinion target, and sentiment polarity specifies whether the opinion is positive, negative, or neutral. This fine-grained analysis allows for amore detailed understanding of opinions and sentiments expressed in the text.  \nStructured sentiment analysis (Barnes et al., 2022) is another formulation of opinion mining, where the nodes are spans of sentiment holders, targets and expressions, and the arcs are the relations between them. Figure 1 illustrates this formulation.  \nnegative  \nI had  \nhoped for better  \nbattery life  \n, as it had only about 2-1/2 hours  \nholder expression target  \ndoing heavy computations (8 threads using 100%) .  \nFigure 1: Structured Sentiment Analysis  \nABSA and structured sentiment analysis overlap significantly in extracting specific opinion facets2. None of the formulations fully incorporate all opinion facets proposed by Liu (2017), which reduces the expressiveness and granularity of the extracted opinions. The example above shows that the opinion is valid for specific individuals or groups engaged in \" doing heavy computations\" . The reason for opinion is also expressed, i.e. “it had only about 2-1/2 hours\". None of the existing opinion mining formulations enable these extractions.  \nThis work investigates semantic representations of opinions to enrich their expressiveness. Towards this end, we studied the specification of opinion for the Semantic Web as described by the Marl","cbCainDTlg0l0vXT","https://ap.wps.com/l/cbCainDTlg0l0vXT","pdf",362726,12,"English","# Introduction\n## Opinion mining background\n## Aspect-based and structured sentiment analysis\n## Motivation for semantic integration\n# Related Work\n## Opinion Mining in NLP","[{\"question\":\"What does the Unified Opinion Concepts (UOC) ontology aim to achieve?\",\"answer\":\"It provides a unified conceptual framework to represent opinions semantically, consolidating opinion facets and enabling structured, unambiguous interpretation across formulations.\"},{\"question\":\"What is the Unified Opinion Concept Extraction (UOCE) task?\",\"answer\":\"UOCE is an NLP task focused on extracting opinions from text with enhanced expressivity, grounded in the rich semantic representation of the UOC ontology.\"},{\"question\":\"How is the UOCE evaluation handled in the paper?\",\"answer\":\"The authors extend and re-annotate an existing opinion mining dataset and introduce tailored evaluation metrics that assess how well extracted opinions adhere to UOC semantics.\"}]","Towards Semantic Integration of Opinions - Unified Opinion Concepts - Ontology and Extraction Task | PDF"]