Machine Learning vs. Deep Learning for Starbucks Review Sentiment Analysis

A new research paper compares the effectiveness of various machine learning and deep learning models for analyzing consumer sentiment in the retail coffee sector. The study focused on Starbucks reviews from ConsumerAffairs.com, finding that while Support Vector Machines (SVM) performed best among traditional machine learning models with 91.0% accuracy, Bidirectional LSTM demonstrated superior performance among deep learning models. The research also highlighted that class imbalance in the dataset negatively impacted positive sentiment recall for several models, underscoring the importance of model selection and preprocessing for customer experience analytics. AI

IMPACT Provides insights into optimal model selection for sentiment analysis in customer experience analytics.

RANK_REASON The cluster contains a research paper detailing comparative analysis of machine learning and deep learning models for sentiment analysis.

Read on Hugging Face Daily Papers →

• CNN
• ConsumerAffairs.com
• convolutional neural network
• decision tree
• gated recurrent unit
• logistic regression model
• long short-term memory
• Md. Alamgir Hossain
• naive Bayes classifier
• random forest
• recurrent neural network
• Starbucks
• support vector machine

• paper
• product

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