A Scalable Unsupervised Framework for multi-aspect labeling of Multilingual and Multi-Domain Review Data (2505.09286v1)

Abstract: Effectively analyzing online review data is essential across industries. However, many existing studies are limited to specific domains and languages or depend on supervised learning approaches that require large-scale labeled datasets. To address these limitations, we propose a multilingual, scalable, and unsupervised framework for cross-domain aspect detection. This framework is designed for multi-aspect labeling of multilingual and multi-domain review data. In this study, we apply automatic labeling to Korean and English review datasets spanning various domains and assess the quality of the generated labels through extensive experiments. Aspect category candidates are first extracted through clustering, and each review is then represented as an aspect-aware embedding vector using negative sampling. To evaluate the framework, we conduct multi-aspect labeling and fine-tune several pretrained LLMs to measure the effectiveness of the automatically generated labels. Results show that these models achieve high performance, demonstrating that the labels are suitable for training. Furthermore, comparisons with publicly available LLMs highlight the framework's superior consistency and scalability when processing large-scale data. A human evaluation also confirms that the quality of the automatic labels is comparable to those created manually. This study demonstrates the potential of a robust multi-aspect labeling approach that overcomes limitations of supervised methods and is adaptable to multilingual, multi-domain environments. Future research will explore automatic review summarization and the integration of artificial intelligence agents to further improve the efficiency and depth of review analysis.

A Scalable Unsupervised Framework for Multi-Aspect Labeling of Multilingual and Multi-Domain Review Data

The paper by Park and Kim presents "MUSCAD" (Multilingual and Scalable framework for Cross-domain Aspect Detection), an unsupervised framework designed to efficiently label multiple aspects of multilingual and multi-domain review data. The researchers address critical challenges in current aspect-based analysis methodologies, particularly their reliance on domain-specific supervised learning models that are costly and cumbersome due to the need for vast labeled datasets.

Summary of the Framework

The MUSCAD framework utilizes a fully unsupervised approach to execute multi-aspect labeling across different languages and domains without manual intervention. This is achieved through a combination of K-means clustering, which automatically generates initial aspect category candidates from unlabeled data, and aspect-aware embedding that is fine-tuned using self-attention and multi-head attention mechanisms. The approach is innovative in its application of the Max-Margin Loss function, which facilitates the optimization of these embeddings by reducing the similarity to negative samples.

Key Results and Findings

The research evaluates MUSCAD by applying it to multilingual datasets, specifically Korean and English reviews related to the hotel, food, and beauty industries. The efficacy of the framework is validated through extensive experiments that include automatic labeling processes and human evaluations, confirming the quality of the generated labels as comparable to expert-level manual annotations. The authors report strong F1-scores for fine-tuned classification models trained on MUSCAD outputs, surpassing performance when compared directly to LLMs.

Critical Analysis

• Multilingual and Multi-domain Applicability: Unlike traditional sentiment analysis methods constrained by language and field specificity, MUSCAD demonstrates adaptability across linguistic boundaries and various market domains. This trait is evidenced by its robust performance on both Korean and English datasets encompassing different industries.
• Cost-Effectiveness and Scalability: By bypassing the necessity for labeled data, the framework significantly reduces the costs associated with data labeling and provides a scalable solution suitable for rapid deployment in real-world operations.
• Performance Metrics: The system achieves high precision in automatic labeling, further validated through fine-tuning experiments with existing pre-trained classifiers, and also confirms that unsupervised labeling performs well against LLMs under economically viable conditions.

Implications and Future Directions

Practically, the MUSCAD framework can revolutionize real-time aspect-based sentiment analysis by eliminating human-dependent data preprocessing and by expediting the data labeling process in varied industries. From a theoretical perspective, it challenges the prevailing dependence on labeled datasets and could stimulate further research into unsupervised methodologies for NLP tasks.

For future research, the authors plan to explore automated summarization of reviews and integration with AI agents to enhance this framework's capabilities, potentially leading to fully autonomous, real-time review systems that adapt dynamically to new languages and domains.

In conclusion, this paper offers a compelling extension to aspect-based review analysis. By addressing significant limitations in current methodologies with a novel unsupervised framework, it sets a promising foundation for future advances in automated sentiment analysis systems.