Towards Semantic Integration of Opinions: Unified Opinion Concepts Ontology and Extraction Task (2025)

Abstract: This 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.

## introduction opinion foot_0 mining has seen a move from a traditional sentence-and document-level analysis @xcite 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. @xcite @xcite . the task focuses on identifying the aspects of the entities and their associated sentiments from a given text sequence. in the following sentence: absa results are extracted as the following tuple: {battery life, battery#operational_performance, negative}. 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 a more detailed understanding of opinions and sentiments expressed in the text. structured sentiment analysis @xcite 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. i had hoped for better battery life , as it had only about 2-1/2 hours doing heavy computations (8 threads using 100%). absa and structured sentiment analysis overlap significantly in extracting specific opinion facets foot_1 . none of the formulations fully incorporate all opinion facets proposed by @xcite , 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. this 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 ontology @xcite . however, it has a limited cross-compatibility with the opinion formulations researched in nlp. we unify the opinion facets studied extensively in nlp with the semantic structures described in the marl ontology to develop a comprehensive unified opinion concepts (uoc) framework. the uoc ontology consolidates and formalises these opinion components into an exhaustive set, enabling the semantic representation of opinions in a structured and unambiguous manner. uoc leverages the implicit hierarchies and relationships across diverse nlp frameworks based on the theoretical foundations of @xcite . our contribution 3 can be summarized as follows: ## unified opinion concept ontology one of the primary objectives of this work is the development of an ontology to describe opinions uoc: reason + reason_span: @xmath0:string uoc: qualifier + qualif ier_span: @xmath1:string uoc: holder + holder_span: @xmath2:string holder entit y rdfs:literal uoc:sentiment +sentiment_expression: @xmath3:string uoc: aspect + aspect_term: @xmath4:string uoc: sentiment intensit y weak average strong uoc: sentiment polarit y positive neutral negative hasholder entity hasintensity haspolarity embodies aspect hastarget entity hasreason hasqualifier isheldby uoc: target uoc: opinion conveys sentiment isexpressed ontarget target entit y rdfs:literal aspect category rdfs:literal hasaspectcategory figure 2: unified opinions concepts (uoc) ontology diagram and the associated semantics precisely. an ontology is an explicit, machine-readable specification of a shared conceptualisation. the uoc ontology shown in fig. 2 describes the following components: (i) classes conceptualising opinion and its facets, (ii) attributes of classes along with the datatype property (+attribute_name:@xmath5, and (iii) object properties, that describe relationships between the concepts represented by the classes. we formalize the ontology of opinions through a two-step process. first, we identify tasks within the domain of opinion mining and examine the overlap of their facets with the concepts in the marl ontology. these facets and concepts are then aligned and integrated to establish a unified representation of opinion concepts, we refer to as unified opinion concepts (uocs). ## unified opinion concept extraction (uoce) we harness the rich semantics of the uoc ontology to propose unified opinion concept extraction (uoce), an nlp task for comprehensive opinion extraction. to facilitate uoce solutions, we provide (i) the formalized problem definition, (ii) the evaluation metrics, (iii) the analysis of existing datasets and the extension of annotations for method evaluation, and (iv) baseline methods with llms. ## conclusion this paper introduced the unified opinion concepts (uoc) ontology, which integrates the diverse perspectives on opinion mining task descriptions in nlp based on @xcite and the ontological opinion representation @xcite . uoc formalizes the semantic structure of opinions previously expressed implicitly and scattered across the opinion-mining literature. we proposed unified opinion concept extraction (uoce) as an nlp task based on the expressive semantics of the uoc ontology. to facilitate system development for uoce, an evaluation dataset that extends the annotations of a gold standard dataset is also provided. we also introduced tailored evaluation metrics for the extracted opinions, comparing them with traditional metrics for fine-grained opinion-mining tasks. finally, we provided baseline methods the unified opinion concepts (uoc) ontology offers an expressive framework for semantically structured opinion mining, yet several limitations must be acknowledged. firstly, the evaluation dataset provided is helpful for evaluation purposes but is insufficient in size to train a practical system using data-driven approaches. the only training data points we used for our baseline approaches were the in-context examples in the prompt. secondly, even after incorporating element-wise exact matches, current evaluation metrics rely on overlapping extracted or generated opinion concepts with the gold labels. they penalize any lack of exact matching between predicted tokens and reference labels. this strictness mainly affects the evaluation of reasons and qualifiers, which often have considerable token spans. therefore, adopting flexible and context-aware evaluation metrics would significantly benefit this research. lastly, the established baselines open significant scope for exploring effective machine learning techniques to enhance performance. evaluating different modelling approaches, such as transfer learning and graph machine learning, is essential to understand better and utilize the comprehensive semantic structure introduced in this work.