Shoes- ACOSI : A Dataset for Aspect-Based Sentiment Analysis with Implicit Opinion Extraction (2024)

Abstract: We explore *implicit opinion extraction* as a new component of aspect-based sentiment analysis (ABSA) systems. Prior work in ABSA has investigated opinion extraction as an important subtask, however, these works only label concise, *explicitly*-stated opinion spans. In this work, we present **Shoes-ACOSI**, a new and challenging ABSA dataset in the e-commerce domain with implicit opinion span annotations, the first of its kind. Shoes-ACOSI builds upon the existing Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction task, extending the task to quintuple extraction—now localizing and differentiating both implicit and explicit opinion. In addition to the new annotation schema, our dataset contains paragraph-length inputs which, importantly, present complex challenges through increased input length, increased number of sentiment expressions, and more mixed-sentiment-polarity examples when compared with existing benchmarks. We quantify the difficulty of our new dataset by evaluating with state-of-the-art fully-supervised and prompted-LLM baselines. We find our dataset presents significant challenges for both supervised models and LLMs, particularly from the new implicit opinion extraction component of the ACOSI task, highlighting the need for continued research into implicit opinion understanding.

## introduction aspect-based sentiment analysis (absa) is an important type of fine-grained sentiment analysis, with critical applications in areas such as healthcare, product review analysis, and argument analysis @xcite . popular absa subproblems include extracting aspect terms and their corresponding aspect categories, finding supporting opinion spans, and classifying the sentiment polarity. in particular, the aspect-category-opinion-sentiment (acos) absa task input text looks nice, and the surface is smooth, but certain apps take seconds to respond. ## related work many efforts have been made to grow the absa task, with significant effort placed into developing datasets @xcite @xcite @xcite as well as improved methods @xcite @xcite @xcite @xcite for the burgeoning acos task. these works have been instrumental in pushing the boundaries of the absa field. implicit opinion has been an area of research within absa @xcite . @xcite and @xcite note the need for implicit opinion extraction, with real-world applications including implicit opinion mining and identifying latent needs expressed in customer reviews with the goal of improving product designs. however, absa datasets which identify implicit opinion ignore the task of extracting localized spans @xcite , introducing a gap between existing evaluation benchmarks and realworld absa applications. our work addresses this gap by expanding the scope of absa to distin-guish and extract both explicit and implicit opinions within text. ## shoes-acosi dataset shoes-acosi is the first absa dataset to include implicit opinion span annotations. below we outline the dataset, curation process, and notable characteristics. for the purposes of this task, we define an implicit sentiment opinion as a span containing nuanced and potentially ambiguous sentiment language whose meaning can be resolved only via thorough contextualization from the domain and full input. ## evaluation setup we benchmark our dataset, comparing two fully supervised absa models that perform strongly on the acos task, as well as few-shot prompted llms using an absa-specific prompting method. for the supervised models (mvp, gen-scl-nat), we modify their output target formats slightly to account for the new implicit/explicit flag that forms the fifth acosi tuple component. appendix c and d.1.1 respectively contain more details for the fully supervised and llm setups. ## results & analysis ## conclusion shoes-acosi and the acosi task introduce a new and challenging problem for the aspect-based sentiment analysis community. notably, shoes-acosi introduces the challenge of implicit opinion extraction, which poses problems for both fully supervised models and, in particular, llm approaches which are confined to few-shot in-context demonstrations. our dataset presents a novel challenge and motivates a new and important direction of research for aspect-based sentiment analysis.