Predicting Customer Frustration Before Formal Complaint: A Deep Learning Approach for Proactive Customer Service

This study explores the application of machine-learning and deep-learning techniques to customer sentiment analysis as a potential foundation for proactive customer service. Using the Twitter US Airline Sentiment dataset, the study compares Logistic Regression, Multinomial Naive Bayes, Random Forest, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU) models for classifying customer messages as positive, neutral, or negative. The research examines model performance using classification metrics and discusses the challenges associated with interpreting customer sentiment, including sarcasm, contextual ambiguity, and limitations in individual-message analysis. The study also explores the potential role of automated sentiment analysis in proactive customer-service systems and identifies sequential customer-interaction data as an important direction for future research into early complaint prediction.