Computational Linguistics Based Emotion Detection and Classification Model on Social Networking Data

Abstract

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models.