Opinion Units: Concise and Contextualized Representations for Aspect-Based Sentiment Analysis (2025)

Abstract: We introduce opinion units, a contribution to the field Aspect-Based Sentiment Analysis (ABSA) that extends aspect- sentiment pairs by including substantiating excerpts, derived through hybrid abstractive-extractive summarisation. The goal is to provide fine-grained information without sacrificing succinctness and abstraction. Evaluations on review datasets demonstrate that large language models (LLMs) can accurately extract opinion units through few-shot learning. The main types of errors are providing incomplete contexts for opinions and and mischaracterising objective statements as opinions. The method reduces the need for labelled data and allows the LLM to dynamically define aspect types. As a practical evaluation, we present a case study on similarity search across academic datasets and public review data. The results indicate that searches leveraging opinion units are more successful than those relying on traditional data-segmentation strategies, showing robustness across datasets and embeddings.

## introduction we propose opinion units as a representation for subjective viewpoints in text. an opinion unit consists of (i) an aspect such as price, quality, or location, (ii) an excerpt, which may be lightly paraphrased to only include relevant text, that contextualises the opinion (iii) and a sentiment such as positive, negative or neutral. the structured nature of opinion units makes them suitable for applications requiring fine-grained aspect-based sentiment analysis (absa), such as the mining and retrieval of opinions. absa goes beyond the surface level of traditional sentiment analysis. instead of assigning a sentiment to an entire text, absa identifies opinions expressed about particular features of, for instance, a product, service or event. this multi-faceted analysis provides valuable insights for those seeking to understanding public opinion on a particular topic. for example, for retailers, absa of customer reviews or interactions can suggest areas for improvement, personalise marketing strategies, and gauge overall customer satisfaction. initial absa research focused on classifying reviews into predefined aspect-and sentiment categories @xcite . over time, this came to include the extraction of aspect-and sentiment keywords @xcite . while the reduction of a text to keywords is helpful for many applications, it also lead to information loss. in contrast, opinion units offer a structured representation that retains more of the original nuance. the emergence of generative llms, with their capacity for longer sequence-to-sequence outputs, enable the flexible extraction of phrases required for creating opinion units. for concisely expressed opinions, as in the short-sentence examples used in keyword-extraction benchmarks like semeval @xcite , opinion units closely resembles keyword extraction. however, in real-world reviews, customer opinions often involve descriptions and motivations spanning longer passages. phrase extraction provides a more natural and expressive method for capturing these nuanced opinions. for instance keyword extraction would overlook subtlety in a sentence like: "the outdoor area is delightful, especially in the evening, with its soft lighting and comfortable chairs creating a cozy atmosphere". moreover, phrases provide better interpretability for end users, allowing them to identify which sections of the raw text influenced the decisions made by downstream applications. the extraction of opinion units can serve as a standalone chunking strategy for applications requiring detailed information. however, it can also be made as preprocessing step before keyword extraction @xcite , because the last sunday we went to brunch and i had a muffin. it was amazing! we loved our waiter stephanie she was so friendly however the service could have been a little quicker. but on the whole, we had a great time! ➤ muffin: i had a muffin. it was amazing. {positive} ➤ staff friendliness: we loved our waiter stephanie, she was so friendly. {positive} ➤ service speed: the service could have been a little quicker. {negative} ➤ overall brunch experience: on the whole, we had a great time. {positive} figure 1 : four opinion units extracted from a review, each representing an opinion in the text and consisting of an aspect label, an excerpt from the text, and a sentiment label. the colour purple indicates aspects, and orange indicates sentiment terms. in this article, we explore how opinion units can be extracted from subjective commentary, specifically customer reviews, by large language models (llms). the models are prompted in a way that allows them to dynamically generate aspect categories not explicitly mentioned in the text, and to choose and paraphrase motivating text excerpts that retain only the most relevant information. an example of how opinion units are formed is given in figure 1 and a formal definition is provided in section 3. the main benefit opinion units is that they provide a structured representation of the opinions expressed in a text, while retaining much of the nuance through the supportive excerpt. language models excel at many of the tasks involved in the generation of opinion units, including information extraction, text summarization, entity recognition, and sentiment analysis. previous work has successfully applied llms to extract propositions, that is, atomic factual statements, to facilitate question answering in a dense retrieval setting where both the query and documents are transformed into embeddings @xcite . we transfer this method to the absa domain, demonstrating that llms can effectively identify opinion aspects, extract concise snippets of text expressing the opinion, and accurately classify the sentiment of the excerpt. an important advantage of extracting opinion units with llms stems from the fewshot approach. unlike traditional absa methods that often rely on pre-defined categories or require labeled training data, llms can extract opinion units without such constraints. this opens doors for broader application across diverse domains and allows for more efficient and scalable analysis. in the following sections, we first investigate the ability of llms in generating opinion units by evaluating gpt-4-turbo, gpt-3.5-turbo, and llama2-70b. this evaluation is conducted on subsets of se-meval restaurant review sentence dataset @xcite as well as a yelp dataset (yelp, 2015) containing complete restaurant reviews. furthermore, we categorize the errors produced by the llms, where providing incomplete context, missing aspects and the conflation of objective statements with opinions turn out to be the most serious sources of error. finally, we demonstrate the effectiveness of opinion units in dense similarity search, where words are represented by embeddings. in particular, we show that opinion units outperform the competing chunking strategies of sentence and passage chunking. these positive results suggest that opinion units are potentially useful also for dense retrieval, retrieval-augmented generation and clustering applications. for example, in topic modeling, opinion units can reveal which topics customers discuss in reviews. the experiments conducted in this article serve to answer the following research questions: rq1. to what extent can llms extract accurate opinion units? rq2. what are the types and frequencies of errors made by the llms in this process? rq3. how does the performance of opinion units in dense similarity search for opinions compare to other data-segmentation strategies? this section recalls related work on absa, summarisation, and information retrieval. ## opinion units as stated in section 1, an opinion unit is composed of three elements: i) an aspect label, ii) a text ex-challenge example of review and extracted opinion units benefits of opinion units ## method the experimental evaluation of opinion units comprises two parts. first, we evaluate the performance of three llms (gpt-4 turbo, gpt-3.5 turbo, and llama2-70b) in generating well-formed opinion units. second, we perform a case study on opinion retrieval, comparing data segmentation based on opinion units to traditional chunking strategies. ## summary and conclusion we have presented opinion units as a structured representation for subjective viewpoints, enhancing traditional aspect-sentiment pairs by incorporating substantiating excerpts that retain detailed information. opinion units can function as an independent chunking strategy for applications that require detailed information or be utilized as a preprocessing step that allows for further abstractions such as category classification or keyword extraction. our findings demonstrate the ability of llms to accurately extract opinion units from restaurant review datasets datasets. the most frequent errors were insufficient excerpt context and misclassifying nonopinion statements as opinions. furthermore, a case study showcased the effectiveness of opinion units in opinion retrieval using dense embeddings, outperforming traditional segmentation methods. the few-shot approach allows the llm to identify aspects without annotated data or predefined aspect categories. each opinion unit represents a single opinion, consisting of an aspect label, a text excerpt that provides context, and a sentiment label that conveys the expressed sentiment. these units facilitate downstream applications, e.g., clus-tering and retrieval. the excerpt generation handles difficulties such as intertwined opinions, where discussions interleave opinions with other topics, and multi-sentence opinions. furthermore, the sentiment label allows for filtering at inference time, mitigating the issue with word embeddings where words with contrasting sentiment polarities have similar vector representations @xcite .