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Top1. Introduction
Natural Language Processing (NLP) represents the domain of Linguistics and Artificial Intelligence (Alloulbi et al., 2022) that contributed to making computers understand the words or statements written in languages understood by a humans. NLP serves the needs of users who lack the time to learn new languages or become proficient in them because not all users are fluent in a particular language. NLP aims to collect one or more specialism techniques or models (Asderah and Kalkur, 2017). The measure of NLP assesses an algorithmic module and permits the combination of language generation and language understanding (Mieruszewska et al., 2021). It is even utilized in detecting multilingual events (Khurana et al., 2017; Jarosz and Suchanek, 2021). The enriching standard added a diverse interweave to this domain. For instance, the declaration that follows can be interpreted differently. The social media infrastructure in this area presents both challenges and opportunities. It provided online writers with anonymity and allowed them to freely express their emotions. Additionally, the data are amassed over a specified period and can demonstrate a crucial function in ensuring reliability (Rajput, 2020).
The huge range of educational and engineering applications (Al-Saedi et al., 2019) and the elevated growth of Web 2.0 led to sentiment analysis (Shahzad et al., 2023; Veni et al., 2021) an emerging research domain in mining data and NLP. Thus, several techniques and applications have been devised recently to specify the document polarity. The detection of polarity indicates an imperative dowel in the majority of assessment tools (Xia et al., 2015) (Basiri et al., 2021). The analysis of sentiment is also known as opinion mining and the analysis is extensively considered in social networks and other domains (Zhou et al., 2019a; Zhou et al., 2019b; Jin et al., 2020). The analysis of sentiment (Poecze et al., 2022) is the process of quantification and identification of sentiment in reviews. Based on reviews, the companies that supply products can revolutionize their items by assigning several possessions to design or maximize the satisfaction of its users and brand status (Aydin and Güngör, 2020). The goal of sentiment analysis is to analyze and mine knowledge using the subjective information available on the network (Basiri et al., 2021). The sentiment assessment (Haque et al., 2023; Talaat, 2023) helps to evaluate text connotation, and sentiment concealed in the text by utilizing the processing of natural language technologies to categorize text (O’Connor et al., 2010; Jin et al., 2020).