## 1 Introduction  
Computational linguistics has become a significant area in the domain of linguistics. Computational techniques employed in computational linguistics have their origin in artificial intelligence (AI) or computer science. However, the main objective of computational linguistics will remain the modeling of human language, and thus it lasts as a part of the humanity domain [1]. Computational linguistics will study how language models should be constructed for better understanding by the computers, that is it not just studies the usage of language in human actions, but even implements particular formal techniques that permit the exact construction of the hypotheses and its successive automated evaluation utilizing linguistic data (corpora) [2]. The formal component of computational linguistics relies on AI techniques [3; 4; 5].  
Sentiment analysis (SA) turns into a growing domain at the intersection of computer science and linguistics that endeavours to automatically decide the sentiment presented in text. Sentiment is categorized into negative or positive evaluations articulated via languages [6]. The most appropriate sources for opinionated text were posts from social networking sites and their observation has obtained popularity as many approaches were available for executing SA, evaluating the thoughts articulated regarding public figures or present events, and categorizing them as negative or positive [7]. SA can be utilized for describing the text categorization as articulating a negative or positive opinion. Emotion detection in text can be projected as a solution for such difficulties. This method does not concentrate on the negative or positive opinions articulated, but rather endeavours to determine the human emotion that can be expressed [8; 9; 10].  
Finding the emotions articulated by an individual becomes a very difficult task that humans may also struggle with [11]. Trying to devise this identification and establish an automatic way of detecting the expressed emotion is also challenging, not just because of the inadequate accessibility of training data but also the limited data presented in a short text. The emotional categories which are chosen were not exclusively dependent on the research conducted by psychologists with regard to the emotion theory [12]. Several systems adapt the emotion categories on the basis of their findings, separating or merging emotions, without any concern for the scientific standard. In contrast, many accessible systems constitute their training data on the basis of the presence of particular keywords [13]. By utilizing merely keywords-related techniques, there is no way for validating the accurateness of the classifier techniques as the classifier was almost exclusively trained for recognizing such keywords and categorizing the text, respectively [14; 15].  
This article develops a computational linguistics-based emotion detection and classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique 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 the 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 through the use of shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND method can be tested by making use of the benchmark dataset.