## 3 Proposed Model
### Multi-Fold Sentiment Modeling Method (MFSC)

The majority of the current research focuses on document-level text content modeling and sentiment feature extraction, with minimal attention paid to the interaction and correlation among sentences in the document. Between successive sentences in the text, there are evident progressive (forward) and adversative (reverse) linkages, as well as clear correlation and reciprocal influences between terms. As a result, the technique is suggested here for multi-fold sentiment modeling. The extraction of sentiment features and modeling content of text at several levels, such as words, phrases, and documents, helps address the lack of context semantics in dataset texts. The multi-fold sentiment modeling method has three stages, the (i) word, (ii) sentence, and (iii) document levels. In the first fold of words, the input is the outcome of the segmentation 'of sentences. The outcome of this process is the representation of the word vector for the given sentences. In the second fold, i.e., the sentence level, the input for the model is the representation of vectorized words of the given set of sentences, and the outcome is the representation of vectorized sentences from the set of sentences. The multi-dimensional sentiment model is described in detail in the next section. The vectorized collection of several sentences is provided as the input in the document fold, and the result is the vectorized document. The specifics at the document level are listed below. 1. Based on the grammatical rules and conjunctions between sentences, two types of relations are obtained: forward relations and reverse relations;
2. The attention-based network is provided with prior knowledge of the following two types of relationships between sentences. Sentences with a reverse connection should have opposing sentiment polarities as much as is feasible. Sentences with forwarding relationships should have uniform sentiment polarity as much as is feasible. An attention-based system at the sentence level that is based on relationship constraints between sentences is provided here. This mechanism takes into account the two different sorts of linkages that exist between sentences. In the research, the attention-based method utilizes the attention formula at the phrase level;
3. The vectorized text of every phrase is provided as the input for the dual-LSTM network based on the limitations of the attention-based mechanism, and the vectorized view of the given document is collected. An output for sentiment categorization is generated by a multi-layer perception network using the representation of a vectorized document that has been obtained. Equation (1) provides a definition of the sentiment classification function that is based on multi-fold and multi-dimensional sentiment modeling:

\[\min_{x}\sum\
olimits_{j=1}^{M}\left(x^{T}y_{j}-z_{j}\right)^{2}+\partial_{1} x_{1}+\partial_{2}\sum\
olimits_{j=1}^{M}\sum\
olimits_{k\
eq j}S_{jk}\left( \omega_{j}-\omega_{k}\right)^{2}+\partial_{3}\sum\
olimits_{j=1}^{M}\sum \
olimits_{k\
eq j}P_{jk}\left(\mu_{j}-\mu_{k}\right)^{2} \tag{1}\]

Here, the total number of texts is represented by \(M\), which represents the model of the sentiment classification; \(y_{j}\) is the representation of the vector of the \(j\)th text and \(z_{j}\) is the sentimental orientation of the \(j\)th text; \(\omega_{j}\) and \(\omega_{k}\) is the factor of attention for the word level; \(\mu_{j}\) and \(\mu_{k}\) is the factor of attention for the sentence level; \(S_{jk}\) is the factor of similarity of sentiment text \(j\) and sentiment phrase \(k\); \(P_{jk}\) is the similarity factor of sentence \(j\) and sentence \(k\); \(\partial_{1}\), \(\partial_{2}\), and \(\partial_{3}\) represent the various hyperparameters. ### Multi-Dimensional Sentiment Classification Method (MDSC)

The primary actions involved in multi-dimensional sentiment modeling at the level of individual words are discussed below:

1.