Problem
Research questions and friction points this paper is trying to address.
Aspect-Based Sentiment Analysis
evaluation-annotated corpus
aspect-value pairs
e-commerce reviews
fine-grained sentiment analysis
SSP-based construction of evaluation-annotated data for fine-grained aspect-based sentiment analysis
🤖 AI Summary
This study addresses the scarcity of high-quality annotated data for fine-grained aspect-based sentiment analysis in Korean e-commerce reviews by introducing EVAD, a novel Korean fashion-domain review dataset. The authors propose an extended ABSA framework that supports unary, binary, and multi-value aspect classification, innovatively modeling aspect values according to their value types. They achieve efficient and precise fine-grained annotation by integrating semi-supervised symbol propagation (SSP) with linguistic resources based on finite-state transducers (FSTs). Experimental results demonstrate the quality of the dataset and effectiveness of the approach: KoBERT and KcBERT models trained on EVAD attain F1 scores of 0.88 and 0.90, respectively, on the aspect–value pair extraction task.
📝 Abstract
We report the construction of a Korean evaluation-annotated corpus, hereafter called 'Evaluation Annotated Dataset (EVAD)', and its use in Aspect-Based Sentiment Analysis (ABSA) extended in order to cover e-commerce reviews containing sentiment and non-sentiment linguistic patterns. The annotation process uses Semi-Automatic Symbolic Propagation (SSP). We built extensive linguistic resources formalized as a Finite-State Transducer (FST) to annotate corpora with detailed ABSA components in the fashion e-commerce domain. The ABSA approach is extended, in order to analyze user opinions more accurately and extract more detailed features of targets, by including aspect values in addition to topics and aspects, and by classifying aspectvalue pairs depending whether values are unary, binary, or multiple. For evaluation, the KoBERT and KcBERT models are trained on the annotated dataset, showing robust performances of F1 0.88 and F1 0.90, respectively, on recognition of aspect-value pairs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Semi-Automatic Symbolic Propagation
Aspect-Based Sentiment Analysis
Finite-State Transducer
Aspect-Value Pair
Evaluation-Annotated Corpus
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