This analysis applies large group decision making to assess widely shared complaints in user reviews, suggesting improved interpretability and actionable insights.
Consumer to consumer (C2C) e-commerce platforms allow users to buy and sell second hand products and they offer affordability and support sustainable consumption. In these environments, user generated reviews provide valuable insights into service failures. Traditional sentiment analysis and Aspect Based Sentiment Analysis (ABSA) methods primarily focus on classifying the polarity of opinions expressed in reviews. However, these approaches often fall short in capturing the user agreement or identifying whether specific complaints are widely shared. The present study adopts a Large Group Decision Making framework to analyze low rated Turkish language reviews from a second hand marketplace app. The approach integrates ABSA and semantic similarity modeling to improve the interpretability of user complaints. Also it enables to detect widely shared and divergent complaints and also offers more actionable insights than traditional sentiment aggregation.
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Ahmet Cumhur Öztürk (2025) studied this question.
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