Deep Learning with Natural Language Processing for Emotion Detection and Classification on Social Networking

Emotion detection has emerged as a critical area of study that exposes many relevant inputs. Emotion can be expressed in numerous forms such as written text, facial and speech expressions, and gestures. Emotion analysis represents the task of identifying the attitude against a target or topic. This might be an emotional (positive or negative) or polarity state including anger, joy, or sadness. Emotion detection in a textual document is basically a content-based classification issue such as notion from deep learning (DL) and natural language processing (NLP) sectors. In this aspect, this article develops Deep learning with Natural Language Processing for Emotion Detection and Classification on Social Networking (DLNLP-EDC) approach. The proposed DLNLP-EDC algorithm carries out proper categorization of various emotions in the social networking data. Primarily, the DLNLP-EDC technique employs data preprocessing and word2vec feature extraction. To identify several kinds of emotions, the DLNLP-EDC technique designs capsule autoencoder (CAE) model. For improving the performance of the CAE model, enhanced cuckoo search optimization (ECOA) is derived. The results of the DLNLP-EDC technique are studied well on benchmark databases. The simulation result signified the improvement of the DLNLP-EDC technique over existing systems.