Streaming Text Trend Analysis
Identifying Hot Topic Trends In Streaming Text Data
An NLP model to detect and visualize trending topics from real-time text streams using a sequential processing approach.
Problem Statement
Social media and news platforms generate massive volumes of text data in real-time. Identifying trending topics quickly and accurately is crucial for applications in news aggregation, social listening, and market analysis. Traditional batch processing methods fail to capture the temporal dynamics of emerging trends.
Methodology
Implemented a streaming data pipeline to process text data sequentially. Applied TF-IDF vectorization combined with clustering algorithms to group similar content. Developed a trend detection algorithm based on topic velocity and acceleration metrics across sliding time windows.
Results
Successfully detected emerging topics in near real-time with high accuracy. The sequential approach demonstrated effective trend identification with lower computational overhead compared to distributed alternatives.