## 1 Introduction
For instance, the ImageNet ([[https://www.image-net.org/index.php](https://www.image-net.org/index.php)]([https://www.image-net.org/index.php](https://www.image-net.org/index.php)), (accessed on 1 February 2022)) dataset for visual recognition and the YouTube 8M ([[https://research.google.com/youtube8](https://research.google.com/youtube8)]([https://research.google.com/youtube8](https://research.google.com/youtube8)) m/, (accessed on 1 February 2022)) dataset for video understanding have been vital for deep learning algorithms to make perform accurate recognition. Image and video recognition are generally applied for many purposes, regardless of geographical and cultural boundaries. Text datasets, however, cannot be treated generally. For example, a textual dataset for Arabic cannot be used to train English sentiment analysis or vice versa. Therefore, natural language processing research requires many adjustments due to cultural boundaries. The textual signal naturally consists of non-numeric and unstructured data. Processing unstructured data is a challenge for researchers in NLP, and therefore many researchers have proposed formulas for transforming the unstructured data into a more structured format. A bag of words has been proposed to transform textual data into numerical data by exploiting the frequency of each word in a sentence, resulting in the concepts of Term Frequency and Inverse Document Frequency proposed in [12]. Word embedding methods have been proposed to obtain more redundant data in order to represent words in consideration of their relationship to other words in many contexts. There are a number of word embedding techniques, such as Word2 vec [13], fastText [14], and GloVe [15]. Word embedding requires a massive corpus in order to train the algorithms to calculate the vector values of specific words. Textual signals may come from a number of unofficial sources, such as social media, e-commerce comments, book reviews, and websites. This condition leads to data problems such as duplicated, unnecessary, meaningless, non-mixing, and stop words. Normalizing data before further processing plays a significant role in achieving high classification accuracy. In many languages, such as in English or Indonesian, the meaning of a word rarely changes with the addition of prefixes or suffixes. Therefore, removing prefixes and suffixes can help reduce the number of specific words (terms) in a corpus, thus reducing the computing complexity. However, in Arabic, words may change their meaning entirely with the addition of a prefix or suffix; therefore, stemming might lead to misinterpretation of the sentence. The Arabic language is one of the most widely spoken languages today. It is an official language of the United Nations organization, estimated to be used daily by more than 400 million people. Its usage on the web has shown vigorous growth in recent years, where it ranked as the fourth most used language in cyberspace [16]. The Arabic language can be found in three forms: Classical Arabic (CS), which adheres to strict grammatical and morphological rules and is usually used in literary texts. Second, Modern Standard Arabic (MSA) is commonly used in correspondence and formal speech. Lastly, Dialectical Arabic (DA) refers to oral utterances spoken in daily communication [17]. Typically, microblogging content will be in MSA or DA or a variant of the two where the Arabic words are written using Latin letters, numbers, and punctuation [18]. This research observes varied embedding methods and Long Short-Term Memory (LSTM) with single, double and triple layers to recognize sentiment polarity. The authors adopt the Arabic Sentiment Analysis (ASAD) dataset from [19] to evaluate the proposed frameworks.