Tracking research evolution is necessary for keeping up with the rapid progress of research in all fields. Analyzing the global overview of scientific topics in various fields is a crucial step within the scientific literature, which is necessary for researchers to go on with trending evolution. Many methods have been developed for detecting the topic in social networks, but the limited word frequency, more computational time and sparse nature are the common problems. To eradicate this issue in trending scientific topic detection, a Stock exchange trading light spectrum optimization (SETLSO) enabled Random Multi-Model Deep Learning (RMDL) to be developed. SETLSO is formed by the integration of Stock exchange trading optimization (SETO) and Light Spectrum Optimization (LSO). Here, Bidirectional Encoder Representations from Transformers (BERT) tokenization helps to break sentences into tokens for the input text data. Next to this, the tokenized outcomes are allowed for Aspect Term Extraction (ATE) for extracting aspect terms, followed by the formation of feature vectors. Moreover, the trending scientific topic detection is done employing RMDL, which is optimized by SETLSO. Finally, the performance of SETLSO_RMDL is analyzed by considering various evaluation metrics, such as precision, recall, and F1-score, with superior values of 0.920, 0.924, and 0.914, wherein Mean Square Error (MSE) is least of 0.087 with best-attained value.
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