基于机器学习与深度学习方法的零售咖啡行业消费者评论双模型情感分析
Dual-Model Sentiment Analysis of Consumer Reviews in the Retail Coffee Sector Using Machine Learning and Deep Learning Approaches
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中文总结 AI 辅助
本研究针对零售咖啡行业星巴克评论，对比评估机器学习与深度学习模型的情感分析性能，发现SVM与Bidirectional LSTM表现最优，同时指出类别不平衡会降低正面情感召回率。
中文摘要 AI 辅助
消费者评论在塑造品牌形象和制定商业战略中发挥着重要作用，尤其在零售咖啡这类服务驱动型行业中。本研究提出了一种针对星巴克客户评论的比较情感分析框架，采用经典机器学习与深度学习方法。该数据集采集自ConsumerAffairs，包含700余条评论，通过预处理与探索性数据分析识别时间和地域模式。情感标签通过对星级评分二值化生成，其中4分和5分归类为正面，1至3分归类为负面，所得数据集存在严重的负面情感类别不平衡问题。研究评估了5种机器学习分类器，包括逻辑回归、支持向量机（SVM）、决策树、随机森林和朴素贝叶斯，以及5种深度学习模型：长短期记忆网络（LSTM）、循环神经网络（RNN）、双向长短期记忆网络（Bidirectional LSTM）、门控循环单元（GRU）和卷积神经网络（CNN）。模型性能通过准确率、精确率、召回率和F1值进行评估。在机器学习模型中，SVM取得了最高准确率，达91.0%；而在深度学习模型中，Bidirectional LSTM表现最佳，且对未见过的数据展现出良好的泛化能力。研究还发现，类别不平衡对多个模型的正面情感召回率产生了负面影响。总体而言，本研究对用于现实世界消费者情感分析的机器学习与深度学习方法进行了比较评估，强调了在零售咖啡行业的客户体验分析中，选择合适的模型与预处理方式的重要性。
英文摘要
Consumer reviews play an important role in shaping brand perception and business strategies, particularly in service-driven industries such as retail coffee. This study presents a comparative sentiment analysis framework for Starbucks customer reviews using classical machine learning and deep learning approaches. The dataset, collected from ConsumerAffairs, contains more than 700 reviews and was analyzed through preprocessing and exploratory data analysis to identify temporal and geographic patterns. Sentiment labels were generated by binarizing star ratings, with ratings of 4 and 5 classified as positive and ratings of 1 to 3 as negative. The resulting dataset was substantially imbalanced toward negative sentiment. Five machine learning classifiers, including Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Naive Bayes, were evaluated alongside five deep learning models: LSTM, RNN, Bidirectional LSTM, GRU, and CNN. Model performance was assessed using accuracy, precision, recall, and F1-score. SVM achieved the highest accuracy among the machine learning models at 91.0 percent, while Bidirectional LSTM showed the strongest performance among the deep learning models and demonstrated good generalization on unseen data. The findings also show that class imbalance negatively affected positive sentiment recall across several models. Overall, this study provides a comparative evaluation of machine learning and deep learning approaches for real-world consumer sentiment analysis and highlights the importance of appropriate model selection and preprocessing for customer experience analytics in the retail coffee sector.
