## 1 Introduction  
Internet data grow rapidly, given the preference of citizens to share their views. Through the expansion of social media, people's opinion tools have been updated, and fields such as opinion mining and sentiment analysis have obtained growing demands. Online reviews cannot be overlooked owing to the possible effects that customer feedback may have on companies. A significant number of research practitioners are currently developing structures that can collect information from such feedback to support marketing insight, drive public sentiment, and enhance consumer loyalty. Consequently, opinion analysis was implemented and applied in several study areas and companies. Twitter has been one of the most widely used microblogging services and a fascinating forum for more than 500 million messages per day from about 1.3 billion people. A message on Twitter (similar to a post on Facebook), by a sequence of characters confined to 280-character limit, is posted publicly or by established followers owing to the account's privacy, which is different from other social media websites [1]. However, users struggle to articulate their brains with limited words because they are constrained in character. Twitter is a significant reflection of world affairs, because it is commonly utilized across all classes of people. As a part of the company's review and feedback, Twitter is a key outlet for the new developments appearing on Twitter. These patterns are used to reach commercial objectives, such as viral ads for trend items [2]. Analysis of tweets is important for businesses to establish effective campaign ideas. Twitter users tweet various limitless messages throughout the day that can offer opinions regarding products, companies, items, and public relations. These views may be categorized into three distinct attitudes: negative, positive, and neutral. Each attitude may also be classified into different levels. This method is called a sentiment analysis that evaluates and derives contextual knowledge from raw data. The study area of emotion analysis and the field of natural language processing (NLP) are interlinked. Extracting useful knowledge from naturally written texts allows NLP to resolve the distance between humanity and machine. The goal of text sentiment analysis (SA) is to extract and analyze knowledge from personal data or product reviews and feedback provided on the internet. As a result of its wide range of industrial and academic applications, as well as the rapid growth of social networks, SA has emerged as a key topic in the field of natural language processing (NLP) in recent years [3,4]. In the literature, three different approaches have been used to solve the problem of SA: the lexicon-based approach, standard machine-based approach, and deep learning-based approach. The lexicon-based method uses a glossary of sentiment terms including enhancement and negation to measure the polarity of each phrase. In the standard machine learning-based approach, the classification of opinions is used as a special case of the issue of classification of documents. However, this method depends on the extraction of knowledge from a statement with an opinion polarization. In addition, this extraction can be annotated individually by terms or automatically through sentiment index-like ratings in comments or emoticons used in tweets. The third approach is deep leaning-based approaches, which has two phases. The term embedding in the text corpus is learned in the first phase [5,6]. The second phase focuses on the use of word embedding to create interpretations of sentences of semantic composition using different deep learning techniques [7]. The ability of any classification method may be enhanced by a technique of integrating several predictor outcomes. The voting system creates a standard strategy by compound classifiers on a stand-alone basis; then, their outputs are integrated into the final decision.