Real-time Topic Modeling and Trend Prediction from Twitter Data using LDA and NLP Techniques

Abstract

Among the digital platforms available today, Twitter holds a unique position as a window into real-time public opinion. Every second, the platform ingests thousands of new posts that collectively mirror societal sentiment, surface emerging conversations, and document rapidly evolving narratives — creating both a rich opportunity for knowledge extraction and a formidable technical challenge. This two-phase computational pipeline combines Latent Dirichlet Allocation (LDA) topic modeling with a Natural Language Processing (NLP) prediction layer, enabling the identification and forecasting of trending discussions within live streams of Twitter data. During the first phase, an LDA model ingests incoming tweet batches to uncover latent thematic structures hidden within the data. A collection of NLP-derived signals then feeds the second phase — including measures of sentiment polarity, subjectivity, and named entity recognition outputs — which drive a decision-tree classifier trained to determine whether any given topic is growing or fading in prominence. Experiments on a continuously streaming Twitter corpus demonstrate strong topic coherence at k = 15 topics alongside consistently low processing latency. Practical value has been confirmed across use cases spanning brand reputation management, crisis communications, and political discourse tracking, with an architecture that is both interpretable and horizontally scalable. The system was evaluated on a corpus of approximately 200,000 tweets collected over a 30-day evaluation period spanning diverse trending events, with end-to-end inference latency maintained at a median of 1.6 seconds per five-minute batch — well within the operational requirements of real-time social media monitoring applications.