A Real-Time Quantum-Conscious Multimodal Option Mining Framework using Deep Learning

Jamuna S Murthy, Siddesh G M, Sri Krishna H V, Kedarnath R Gubbi


Sentiment Option Mining in discussions is an arising yet testing AI function. It aims at finding the emotional states & enthusiastic substitutes of expounders associated with a discussion based on their suppositions, which are conveyed by various techniques of data. But there exist an abundance of intra & inter expression collaboration data that influences the feelings of expounders in a perplexing & dynamic manner. Step by step instructions to precisely and completely model convoluted associations is the critical issue of the field. To pervade this break, in this paper, we present an innovative & extensive system for Multimodal Option Mining Framework in discussions, called a “Quantum-Conscious Multimodal Option Mining Framework (QMF)”, which uses the numerical ceremoniousness of quantum hypothesis & a long transient memory organization. In particular, the QMF system comprise of a multiple-modal choice combination method roused by quantum obstruction hypothesis to catch the co- operations inside every expression & a solid feeble impact model motivated by QM hypothesis to demonstrate the communications between nearby expressions. Broad examinations are led on two generally utilized conversational assessment datasets: the MELD & IEMOCAP datasets. The exploratory outcomes manifest that our methodology fundamentally outflanks a broad scope of guidelines & best in class models.


Lexicon, Machine Learning, Multimodal Twitter Sentiment, Context-aware, Optical Character Recognizer

DOI: http://doi.org/10.11591/ijai.v11.i3.pp%25p


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