Hybrid optimisation enabled deep learning for sentiment rating prediction towards text summarisation and question answering system Online publication date: Thu, 30-Nov-2023
by Parsi Kalpana; Pelluri V. Sudha
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 44, No. 4, 2023
Abstract: This work presents a novel question answering (QA) system using deep learning (DL) networks for generating answers to the questions based on review data. Here, QA is performed based on sentiment prediction, and extractive summarisation, wherein hierarchical deep learning for text (HDLTex) is used for sentiment prediction, and deep convolutional neural network (DeepCNN) is employed for generating an extractive summary. Further, a novel optimisation technique named the child drawing African vulture optimisation algorithm (CDAVOA) is devised for training HDLTex and DeepCNN. Further, QA is carried out using the RMDL based on the predicted sentiment and the extractive summary generated. Additionally, the RMDL_QA is examined for its competence based on quantitative measures, like accuracy, F-measure, recall, and precision, and is found to attain values of 0.907, 0.942, 0.958, and 0.926, respectively.
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