用优化算法识别AI生成的假评论，准确率达95.4%。
An Optimized Machine Learning Classifier for Detecting Fake Reviews Using Extracted Features
- 结合文本预处理与多模态特征提取，用哈里斯鹰优化选关键特征。
- 从13539个特征中筛选出1368个，模型准确率达95.40%。
- 适合需要高精度识别假评的电商平台和隐私保护系统使用。
虚假评论损害在线购物的可信度，尤其是人类风格的AI生成评论更难识别。本文提出一种基于机器学习的先进系统，精准分析此类评论。方法融合文本预处理、多模态特征提取、哈里斯鹰优化（HHO）进行特征选择，以及堆叠集成分类器。在包含40,432条原始（OR）与计算机生成（CG）评论的公开数据集上测试，从初始13,539个特征中通过HHO筛选出1,368个，实现89.9%的降维率。最终堆叠模型达95.40%准确率、92.81%精确率、95.01%召回率及93.90% F1分数，证明集成学习与生物启发优化结合在机器生成文本识别中的有效性。由于大规模评论分析常部署于云平台，差分隐私与安全外包等隐私保护技术对用户数据保护至关重要。
原文摘要 · Abstract (English)
It is well known that fraudulent reviews cast doubt on the legitimacy and dependability of online purchases. The most recent development that leads customers towards darkness is the appearance of human reviews in computer-generated (CG) ones. In this work, we present an advanced machine-learning-based system that analyses these reviews produced by AI with remarkable precision. Our method integrates advanced text preprocessing, multi-modal feature extraction, Harris Hawks Optimization (HHO) for feature selection, and a stacking ensemble classifier. We implemented this methodology on a public dataset of 40,432 Original (OR) and Computer-Generated (CG) reviews. From an initial set of 13,539 features, HHO selected the most applicable 1,368 features, achieving an 89.9% dimensionality reduction. Our final stacking model achieved 95.40% accuracy, 92.81% precision, 95.01% recall, and a 93.90% F1-Score, which demonstrates that the combination of ensemble learning and bio-inspired optimisation is an effective method for machine-generated text recognition. Because large-scale review analytics commonly run on cloud platforms, privacy-preserving techniques such as differential approaches and secure outsourcing are essential to protect user data in these systems.
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