Authors - Akshay Ladha, Supraja P Abstract - Twitter social media platforms have become the primary means of communication for customer support, requiring rapid, accurate, and scalable response solutions. Conventional customer support mechanisms are primarily manual and inefficient in handling large volumes of real-time interactions. This paper presents an AI-Assisted Twitter Support System that combines deep learning with distributed streaming engines to automate real-time customer interactions. The system design utilizes Apache Kafka for tweet streaming, Apache Spark Streaming for distributed processing, and Long Short-Term Memory (LSTM) networks for sentiment analysis and multi-class complaint classification. A confidence-aware decision-making module is used to ensure that automated responses are produced only when the prediction confidence level exceeds certain thresholds, thus avoiding potential miscommunications. The system was trained and tested on the Kaggle Airline Sentiment dataset (1,46,400 tweets) with three sentiment classes and eight complaint categories. The sentiment analysis model attained an accuracy of 85.2% (F1-score of 0.846), and the complaint classification model attained an accuracy of 80.5% (F1-score of 0.792). The complete pipeline maintained an average latency of 2.9 seconds with a maximum processing rate of 2500 tweets per minute.
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