Chaotic Sparrow Search Algorithm with Deep Learning for Event Detection and Classification in Social Media Environment

Event detection is a computational process that enables the automatic identification of major events by analyzing media data. An event refers to a significant occurrence that takes place at a specific time and location. Many researchers have focused on predicting certain events through social media analysis. These events include disease outbreaks, election results, stock market trends, the frequency of article citations, product sales, and sports competition outcomes. The noisy nature of media content requires innovative semantic techniques to ensure accurate analysis of media streams. Consequently, improving the accuracy of event detection methods by addressing the noisy characteristics of media content is critical. Recent studies have explored event detection and recognition through social media analysis. This paper introduces a Chaotic Sparrow Search Algorithm with Deep Learning for Event Detection and Classification (CSSA-DLEDC) in social media. The primary goal of the CSSA-DLEDC technique is to identify and classify the presence of events and non-events. To achieve this, the technique involves data pre-processing and the TF-IDF word embedding process. The detection of events in social media is then performed using a Deep Belief Network (DBN). Finally, the hyperparameters of the DBN model are tuned using the Chaotic Sparrow Search Algorithm (CSSA).To validate the improved performance of the CSSA-DLEDC technique, extensive experimental simulations were conducted. The results demonstrated the superior effectiveness of the CSSA-DLEDC technique in the event detection process on social media.