Topicbert: A Cognitive Approach For Topic Detection From Multimodal Post Stream Using Bert And Memory-Graph

Real time nature of social networks with bursty short messages and their respective large data scale spread among vast variety of topics are research interest of many researchers. Many of these researches focus on detection and tracking of hot topics and trending social media events that help revealing many unanswered questions. Research issues such as noisy sentences that adverse grammar and new online user invented words are challenging maintenance of a good methodology; In this research, we use Transformers combined with an incremental community detection algorithm. Transformer in one hand, provides the semantic relation between words in different contexts. On the other hand, the proposed graph mining technique enhances the resulting topics with aid of simple structural rules. Named entity recognition from multimodal data, image and text, labels the named entities with entity type and the extracted topics are tuned using them. All operations of proposed system has been applied with big social data perspective under NoSQL technologies. In order to present a working and systematic solution, we combined MongoDB with Neo4j as two major database systems of our work. The proposed system shows higher precision and recall compared to other methods in three different datasets. (c) 2021 Elsevier Ltd. All rights reserved.