@article{article_1716998, title={Large Group Decision Making for Aspect-Level Consensus Evaluation in Low-Rated App Reviews}, journal={Mühendislik Bilimleri ve Araştırmaları Dergisi}, volume={7}, pages={173–184}, year={2025}, DOI={10.46387/bjesr.1716998}, author={Öztürk, Ahmet Cumhur}, keywords={Büyük Grup Karar Verme, Hedef Tabanlı Duygu Analizi, Cümle Vektörü, Kosinüs Benzerliği, Online Yorumlar}, abstract={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.}, number={2}, publisher={Bandırma Onyedi Eylül Üniversitesi}