Crime Scene Investigation (CSI) is a forensic crime-based series where perpetrators often try to hide their motives to cover up murders. In contrast, investigators trace pieces of evidence to spot culprits. Recognizing the original character played by a particular speaker (i.e., perpetrator, investigator, and suspects), corresponding to any CSI-based dialogue, using textual conversations is challenging. Existing approaches do not use deep multiview learning for processing multiview commonsense-based Knowledge Graph (KG). Our proposed approach, RiMCR, first applies Siamese BERT-Networks (SBERT) to learn sentence structure. We process sixteen multiview relations of commonsense-based knowledge graph \(ATOMIC_{20}^{20}\) through COMET(BART). A dual-view deep network architecture based on independent stacked LSTMs with a self-attention mechanism infuses sequential patterns into sentence and common-sense-based features. Lastly, we concatenate four types of encoded features before passing through the decoder to solve binary and multiclass classification problems. An extensive comparison with sequence models and Large Language Models (LLMs) validates the judiciousness of RiMCR.