## 3 Proposed Model  
The proposed model primarily consists of two major components. Manifold emotion modeling is a technique that incorporates three different components: words, sentences, and documents. The second method makes use of language and punctuation marks to model multi-dimensional sentiments in two dimensions. Each word in the dataset is broken up into its unique phrase by using emoticons as separators. Through the practice of regarding emoticons and linguistic markings as unrecognized words, every sentence is segmented utilizing the word segmentation methodology that is currently in use. A technique for modeling the emotions associated with textual material is presented with three levels: word, phrase, and document. A multi-dimensional technique for classifying sentiment is given for modeling the text content using two dimensions: language-based symbols and emoji symbols at the word and sentence level. The multi-fold with multi-level modeling results are inputs into the multi-level perception network using the pre-trained autoregressive word representation model XLNet to produce the final sentiment classification results (Figure 1). The algorithm of the proposed model is shown as Algorithm 1. The proposed model is divided into four modules. The module-wise discussions of the proposed model are presented below. ```
1:input: IDocument
2:output: IDocumentDVector
3:initialization of the XLNet and Dual-LSTM models
4:IDocumentSVector = []
5:for each sentence in IDocument:
6:for each W_word, emoji in sentence:
7:WVector = BERT(W_word)
8:L_languageWvector = XLNet (L_language)
9:P_emoticonsWVector = XLNet (P_emoticons)
10:sentence WVector = [WVector, emoticon WVector]
11:SVector = Attention(Dual-LSTM(S WVector))
12:L_languageSVector = L_languageWvector
13:sentence SVector = [SVector, L_languageSVector]
14:IDocumentSVector += sentence SVector
15:IDocumentDVector = Attention(Dual-LSTM(IDocumentSVector))

### Pre-Processing

The goal of the pre-processing phase is to remove all extraneous words from the corpus. The following are the major stages of the pre-processing phase:

1. Using the WordPiece tokenization paradigm, each word in the social input text is tokenized and can be broken into several sub-words;
2. The Natural Language Toolkit (NLTK) removes stop words (is, the, a, etc. );
3. Slang is converted to more formal forms;
4. By eliminating texts that include indentations or by employing a widely unused set of suffixes and indentations, such as \"-ing\" or \"pre-,\" one can restore extracted words to the word stem format using a rule-based stemmer technique;
5. Lemmatization removes inflection endings and returns words to the dictionary format. The proposed approach utilizes the NLTK suffix-dropping algorithm for stemming and lemmatization to improve the lexical context and analysis;
6. Uppercase characters are converted to lowercase characters and repeated characters to their generic form;
7. Spelling corrections are made using the Levenshtein distance and by selecting misspelled keywords. Punctuation marks are used to divide cleaned and pre-processed texts into sentences. Punctuation is a collection of symbols that control and clarify the contents of various texts. Punctuation serves to clarify the meanings of texts by connecting or separating words, phrases, and clauses. As a result, punctuation is used to transform words into sentences. Figure 1: The proposed model. XLNet

XLNet is a novel NLP pretraining approach that produces cutting-edge outcomes on several NLP tasks. Autoregressive (AR) language modeling and autoencoding (AE) are two pretraining aims for pretraining neural networks used in transfer learning NLP that have been proven effective. While avoiding the limitations of the two types of language pretraining objectives (AR and AE), XLNet incorporates concepts from both.