Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques
A crucial area of research that can reveal numerous useful insights is emotional recognition. Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-base...
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| Veröffentlicht in: | International journal of communication networks and information security 2022-12, Vol.14 (3), p.176-186 |
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| creator | Mubeen, Suraya Kulkarni, Dr Nandini Tanpoco, Manuel R. Kumar, Dr. R.Dinesh M, Lakshmu Naidu Dhope, Tanuja |
| description | A crucial area of research that can reveal numerous useful insights is emotional recognition. Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-based categorization problem that is at the core of emotion recognition in text documents.This research propose novel technique in linguistic based emotion detection by social media using metaheuristic deep learning architectures. Here the input has been collected as live social media data and processed for noise removal, smoothening and dimensionality reduction. Processed data has been extracted and classified using metaheuristic swarm regressive adversarial kernel component analysis. Experimental analysis has been carried out in terms of precision, accuracy, recall, F-1 score, RMSE and MAP for various social media dataset. |
| doi_str_mv | 10.17762/ijcnis.v14i3.5604 |
| format | Article |
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Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-based categorization problem that is at the core of emotion recognition in text documents.This research propose novel technique in linguistic based emotion detection by social media using metaheuristic deep learning architectures. Here the input has been collected as live social media data and processed for noise removal, smoothening and dimensionality reduction. Processed data has been extracted and classified using metaheuristic swarm regressive adversarial kernel component analysis. 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| subjects | Artificial intelligence Communication Deep learning Digital media Emotion recognition Emotions Heuristic methods Language Linguistics Machine learning Machine translation Natural language Natural language processing Ontology Sentiment analysis Social networks Speech recognition Support vector machines Voice recognition |
| title | Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques |
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