IDENTIFYING HOT TOPIC TRENDS IN STREAMING TEXT DATA USING SEQUENTIAL EVOLUTION MODEL BASED ON DISTRIBUTED

Abstract

Hot topic trends have become increasingly important in the era of social media, as these trends can spread rapidly 
through online platforms and significantly impact public discourse and behavior. As a result, the scope of distributed 
representations has expanded in machine learning and natural language processing. As these approaches can be used 
to effectively identify and analyze hot topic trends in large datasets. However, previous research has shown that 
analyzing sequential periods in data streams to detect hot topic trends can be challenging, particularly when dealing 
with large datasets. Moreover, existing methods often fail to accurately capture the semantic relationships between 
words over different time periods, limiting their effectiveness in trend prediction and relationship analysis. This paper 
aims to utilize a distributed representations approach to detect hot topic trends in streaming text data. For this purpose, 
we build a sequential evolution model for a streaming news website to identify hot topic trends in streaming text data. 
Additionally, we create a visual display model and knowledge graph to further enhance our proposed approach. To 
achieve this, we begin by collecting streaming news data from the web and dividing it chronologically into several 
datasets. In addition, word2vec models are built in different periods for each dataset. Finally, we compare the 
relationship of any target word in sequential word2vec models and analyze its evolutionary process. Experimental 
results show that the proposed method can detect hot topic trends and provide a graphical representation of any raw 
data that cannot be easily designed using traditional methods.