## 3 Proposed Method
### Features Extraction  
We present the significant features generated from the review, and the significance of the feature extraction is to generate highly relevant features that provide sentiment classification and review rating prediction. Hence, the input review \(K_{l,m}\) is fed to the feature extraction phase, wherein the extraction of the SentiWordNet-based statistical features, numerical words, TF-IDF features, hashtags, emoticons, elongated words, punctuation marks, and number of sentences is performed. **SentiWordNet based statistical features:** The SentiWordNet [28] assists in grouping the words into several sets of synonyms known as synsets. Each synset is linked with a polarity score of negative or positive. Here, the score adapts a value in 0 and 1, and its summation offers a value of 1 for each synset. By adapting the cores adapted, it is reliable for making a decision if the evaluation is negative or positive. The words contained in the SentiWordNet are devised based on the parts of speech acquired from WordNet, and thus it uses a program for adapting the scores to each word. Here, the weight tuning of negative and positive score values is given by  
\[[\varphi^{q}(r),\varphi^{q}(s)]=h\big{(}w_{q}\big{)} \tag{2}\]  
where \(\varphi^{q}(r)\) symbolizes the positive score, \(\varphi^{q}(s)\) signifies the negative score, and \(h\) refers to SentiWordNet. Considering the SentiWordNet score, the statistical features, such as mean and variance are evaluated as follows. _(i) Mean:_ It is evaluated by adopting the average of the SentiWordNet score for each word from the review and is expressed as  
\[\mu=\frac{1}{|U(t_{n})|}\times\sum_{n=1}^{|U(t_{n})|}U(t_{n}) \tag{3}\]  
where \(n\) signifies overall words, \(U(t_{n})\) refers to the SentiWordNet score of each review, and \(|U(t_{n})|\) refers to overall scores generated from the word. _(ii) Variance:_ The variance \(\sigma\) is evaluated based on the mean value and is expressed as  
\[\sigma=\frac{\sum_{n=1}^{|U(t_{n})|}|t_{n}-\mu|}{U(t_{n})} \tag{4}\]  
where \(\mu\) symbolizes the mean value. Hence, the SentiWordNet-based feature adapts the positive and negative scores of each word in the review; from that, the statistical features,such as the mean and variance, are evaluated. Thus, the SentiWordNet-based statistical features are expressed as \(C_{1}\). **Numerical words:** The count of the text characters or numerical digits utilized for showing numerals is expressed as \(C_{2}\) with dimension \([10,000\times 1]\), respectively. **TF-IDF:** TF-IDF [29] is utilized for building a composite weight for each term in each of the review data. TF measures how generally a term occurs in the review data, while IDF evaluates how significant a term is. The TF-IDF score is evaluated as  
\[F_{4}=R\left(\frac{\log(1+\phi_{1})}{\log(\phi_{2})}\right) \tag{5}\]  
where \(R\) signifies the total count of the review data, the term frequency is expressed as \(\phi_{1}\), and \(\phi_{2}\) symbolizes the inverse document frequency and refers to the TF-IDF feature with size \([1\times 50]\). **Hashtags:** The hashtag is commonly used to draw concentration, arrange, and promote. The use of hashtags started in Twitter as a means to make it simple for people to determine, follow, and contribute in a conversation. The number of hashtags is evaluated and expressed as \(C_{4}\). **Emoticons:** The emoticons are a pictographic representation of facial appearance using specific characters, typically numbers, punctuation marks or letters. This emoticon assists in articulating the feelings of a person's mood and is a time-saving method. The occurrence of positive and negative emoticons is evaluated and is expressed as \(C_{5}\). **Elongated words:** The feature \(C_{6}\) signifies elongated words, which are characters repeated more than two times in a review, and is expressed as  
\[C_{6}=\sum_{m=1}^{b}\omega_{e}^{m} \tag{6}\]  
where \(\omega_{e}^{m}\) signifies the overall hashtags contained in a \(m\)th review.