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New SSP method enhances sentiment analysis for Korean e-commerce reviews

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for constructing annotated datasets for fine-grained aspect-based sentiment analysis, specifically for Korean e-commerce reviews. This approach, termed Semi-Automatic Symbolic Propagation (SSP), utilizes extensive linguistic resources formalized as Finite-State Transducers. The extended ABSA framework incorporates aspect values alongside topics and aspects, classifying them based on whether values are unary, binary, or multiple. When tested with KoBERT and KcBERT models, the dataset demonstrated strong performance, achieving F1 scores of 0.88 and 0.90, respectively, in recognizing aspect-value pairs. AI

IMPACT Introduces a novel methodology for creating specialized datasets that can improve the accuracy of sentiment analysis models in specific domains.

RANK_REASON The cluster contains an academic paper detailing a new method for dataset construction and its application in sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SSP method enhances sentiment analysis for Korean e-commerce reviews

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The cluster contains an academic paper detailing a new method for dataset construction and its application in sentiment analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Jeesun Nam ·

    SSP-based construction of evaluation-annotated data for fine-grained aspect-based sentiment analysis

    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 patter…

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