标题： CPC-CMS：用于文档级情感分析的认知成对比较分类模型选择框架 显示英文标题

标题： CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis

摘要： 本研究提出了认知成对比较分类模型选择（CPC-CMS）框架，用于文档级情感分析。CPC基于专家知识判断，用于计算评估标准的权重，包括准确性、精确率、召回率、F1分数、特异性、马修斯相关系数（MCC）、科恩的Kappa（Kappa）和效率。朴素贝叶斯、线性支持向量分类（LSVC）、随机森林、逻辑回归、极端梯度提升（XGBoost）、长短期记忆（LSTM）和一种轻量级双向编码器表示来自变压器（ALBERT）被选为分类基线模型。构建了一个包含分类评估分数的加权决策矩阵，以根据标准权重选择最佳分类模型。使用三个社交媒体的公开数据集来展示所提出的CPC-CMS的可行性。根据我们的模拟，在排除时间因素的评估结果中，ALBERT在三个数据集中表现最佳；如果包括时间消耗，则没有单一模型总是优于其他模型。CPC-CMS可以应用于不同领域的其他分类应用。 显示英文摘要

摘要： This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The CPC, based on expert knowledge judgment, is used to calculate the weights of evaluation criteria, including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), Cohen's Kappa (Kappa), and efficiency. Naive Bayes, Linear Support Vector Classification (LSVC), Random Forest, Logistic Regression, Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and A Lite Bidirectional Encoder Representations from Transformers (ALBERT) are chosen as classification baseline models. A weighted decision matrix consisting of classification evaluation scores with respect to criteria weights, is formed to select the best classification model for a classification problem. Three open datasets of social media are used to demonstrate the feasibility of the proposed CPC-CMS. Based on our simulation, for evaluation results excluding the time factor, ALBERT is the best for the three datasets; if time consumption is included, no single model always performs better than the other models. The CPC-CMS can be applied to the other classification applications in different areas.