## 1 Introduction  
The emergence of web 2.0 and Online Social Networking (OSN) sites has provided new dimensions to the communication world and has given ample opportunity to extract provable and countable patterns from public opinions [1]. Therefore, these networks are utilized as powerful methods to identify popularity and trends in various topics, such as politics, entertainment, social or economic problems, and the environment [2]. Notonly do people use standard languages, such as German, Spanish, and English, but they also try to be more advanced by using emotion icons otherwise called hashtags #, URLs, emoticons, etc. [3].  
With the huge volume of content being generated on social networking platforms and the necessity to evaluate it carefully, text classification techniques have been presented in order to handle this sophisticated emergence [4]. In text classification, sarcasm recognition is an important tool that has several implications for numerous fields, including sales, security, and health [5]. Sarcasm means conveying negative opinions through positive words or intensified positive words. On social media, people frequently use sarcasm to express their opinions, and it is inherently tough to analyze not only a machine but also humans [6]. The existence of sarcastic comments has had a crucial impact on sentiment analysis (SA) tasks. For instance, \"It is a great feeling to bring a smartphone which has short battery life.\" is a sarcastic sentence stating negative sentiment regarding battery life utilizing positive words such as \"great feeling\" [7]. Thus, sarcasm detection is an important tool used to enhance SA task performances. Sarcasm detection has been devised as a binary classifier task for the prediction of whether sentences are non-sarcastic or sarcastic.  
Sarcasm is a widely used, well-studied, and well-known topic in linguistics. Despite being part of our speech and so commonly used, it is integrally very difficult for humans and machines to identify sarcasm in text [8]. Since the length of text messages is gradually becoming shorter, the challenge of recognizing sarcasm poses real threats to the efficacy of machine learning algorithms. Hence, it is not important but rather essential to resolve the challenge of sarcasm in text datasets for the refinement and further evolution of different systems applied for sentimental analyses.  
Earlier studies on forecasting sarcastic sentences predominantly concentrated on statistical and rule-based methods, utilizing (1) pragmatic and lexical features and (2) the presence of sentiment shifts, punctuation, interjections, etc. [8]. A deep neural network (DNN) grants a technique the ability to study essential features automatically rather than utilizing handcrafted features [9]. Deep learning (DL) techniques are used in numerous natural language processing (NLP) methods, namely, machine translation, question answering, and text summarization [10]. DL methods have been explored in sarcasm detection, resulting in interesting outcomes.  
This study introduces a new Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection and Classification (HCOA-SACDC) model in the OSN environment. The objective of the HCOA-SACDC method is to determine the existence of sarcasm. To achieve this, the HCOA-SACDC technique pre-processes the input data to make them compatible for further processing. Furthermore, the term frequency-inverse document frequency (TF-IDF) methodology is employed for effective feature extraction. Moreover, the stacked autoencoder (SAE) technique is utilized for the recognition and categorization of sarcasm. Lastly, the HCO approach is exploited to adjust the parameter involved in the SAE, thus increasing detection performance. In the HCO algorithm, significant solutions are created as nests, and the eggs are placed in three varying nests. A comprehensive experimental analysis of a benchmark dataset is performed to highlight the superior outcomes of the HCOA-SACDC model.