## 3 Proposed Method
### Feature Extraction  
The most imperative features were extracted from the pre-processed data \(H_{t}\), wherein statistical, sent word net and classification-specific features were extracted for further Review Rating Prediction and sentiment classification. **(i) SentiWordNet features:** It helps to cluster the words into various groups of synonyms specified as Synsets. Every Synset is connected with a polarity score, such as positive and negative. The score acclimizes the rate in 0 and 1, as well as its summation, providing the rate of 1 for all Synset. It is more dependable to decide whether the evaluation is positive or negative by considering the considered score. Moreover, words included in this feature are specified using parts of speech obtained from WordNet, thereby utilizing a program to designate score to every word. The weight tuning of the positive and negative scores is illustrated by  
\[\left[v^{j}(n),v^{j}(o)\right]=h\left(y_{j}\right) \tag{2}\]  
where \(v^{j}(n)\) denotes positive score, \(v^{j}(o)\) refers to negative score and \(h\) symbolizes SentiWordNet. The extracted SentiWordNet feature is denoted as \(G_{1}\). _Statistical features:_ The statistical features, including variance, mean and standard deviation, were extracted from the pre-processed output. **(a) Mean: It was evaluated by means of SentiWordNet:** average for all words from the pre-processed review data, and given by  
\[\zeta=\frac{1}{\left|\text{ A}(g_{k})\right|}\times\sum_{n=1}^{\left|\text{ A}(g_{k})\right|}\text{ A}(g_{k}) \tag{3}\]  
where \(n\) implies total words, A(\(g_{k}\)) denotes SentiWordNet score of every review and \(\left|\text{A}(g_{k})\right|\) is total scores created from a word and means that the feature is denoted as \(s_{1}\). **(b) Variance:** this feature was computed depending on the mean value, which is illustrated by  
\[\delta=\frac{\sum_{n=1}^{\left|\text{A}(g_{k})\right|}\left|g_{k}-\zeta\right| }{\text{A}(g_{k})} \tag{4}\]  
where \(\zeta\) refers to the mean value and \(s_{2}\) is specified as the variance. **(c) Standard deviation:** this feature was assessed based on the square root of the variance, which indicates the statistic, which gauges a database distribution from the mean rate. The mean difference from every data point was utilized for identifying the standard deviation, and is the same as the square root of variance. The standard deviation is calculated by  
\[\lambda=\sqrt{\frac{1}{Z}\sum_{x=1}^{Z}\left(f_{y}-\delta\right)} \tag{5}\]  
where \(Z\) implies the number of attributes and records in a review data, \(f_{y}\) are terms in a given data, \(\delta\) denotes mean rate and the standard deviation is referred to as \(s_{3}\). The statistical feature is a combination of standard deviation, variance and means feature, which is deliberated as  
\[G_{2}=\{s_{1},s_{2},s_{3}\} \tag{6}\]  
_Classification-Specific Features:_ The classification-specific feature comprises punctuation marks, elongated words, hashtags, emoticons, number of sentences in a review, number of words in a review, average word length, question marks, capitalized words, numerical words and TF-IDF, and these are explained below. **(a) Numerical words:** They are defined as the total amount of numerical digits and text characters exploited for representing numerals, which is denoted as \(c_{1}\). **(b) Punctuation marks:** They are represented as an exclamation mark, dot or apostrophe included in review data, and are denoted as \(c_{2}\). \[c_{2}=\sum_{f=1}^{p}V_{l}^{f} \tag{7}\]  
where \(V_{l}^{f}\) is the total number of punctuations included in the \(f\)th review. **(c) Elongated words:** They are referred to as elongated words, and are a character repeated more than two times in a review data. They are computed by  
\[c_{3}=\sum_{f=1}^{p}\mu_{l}^{f} \tag{8}\]  
where \(\mu_{l}^{f}\) denotes total number of elongated words existing in \(f\)th review.