## 2 Related Work  
Polarity shifting occurs when a phrase's sentiment value changes according to a specific context [27]. The first machine learning methods failed to take account of influences caused by negation structures and other polarity shifting structures. For instance, for early machine learning, the two sentences 'The hotel is very nice but the price is high.' and 'The hotel is very nice, the price is high.' are likely to be classified into the same stage because they contain the same words indicating the sentiment 'nice' and 'high'. To overcome this issue, the works [33] have recently used sequence mining to extract polarity shifting models that inverse, decrease, and eliminate polarity. Using a hybrid of different techniques, SO-CAL (Semantic Orientation CALculator) [23] was one of the first systems to process polarity shifting using rule models and sentiment vocabulary labeled as sentiment datasets, and [34; 35] used dependency grammar to define syntactic rules that identified each negative structure's influence and other polarity shifting structures.  
Ensemble learning is a strong machine learning model that is optimal in classification problems involving many learners; the ability of an ensemble learning model to generalize is much better than that of a single learning model [36]. Ensemble learning is applicable in various domains, including bioinformatics [37], finance [38], and healthcare [39]. The latest research indicates that ensemble learning models could be applied to the sentiment classification problem. Table 1 shows the relevant works conducted over the last ten years that have applied ensemble learning to sentiment classification.  
Apropos the deep learning use for sentiment classification, this approach has recently been recognized as a strong machine learning model and has produced advanced results in various domains to which it has been applied--from computer vision and speech processing to natural language processing [44]. The application of deep learning to sentiment classification has also become more popular. Some recent research studies on sentiment classification that engage in maneuvering deep learning are depicted in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Works** & **Features** & **Learning Models** & **Languages** \\ \hline \hline \multirow{4}{*}{**[PERSON] et al. [40]**} & \multirow{4}{*}{n-gram} & Naive Bayes, Maximum & \multirow{4}{*}{English} \\  & & Entropy and Support & & \\  & & Vector Machines & & \\  & & Support Vector & & \\
**[PERSON] et al. [41]** & n-gram, lexicon & Machines, k-Nearest & & Chinese \\  & & Neighbors, Scoring & & \\  & & Naive Bayes, k-Nearest & & \\
**[PERSON] et al. [42]** & n-gram & Neighbors, Maximum & & \\  & & Entropy, Support Vector & & \\
**[PERSON] et al. [43]** & n-gram & Support Vector Machines, & & \\  & & Logistic Regression & & \\
**[PERSON] et al. [5]** & n-gram & Support Vector Machines, & & \\  & & Logistic Regression & & \\
**[PERSON] et al. [32]** & n-gram lexicon, word & Scoring, Naive Bayes, & & \\  & & Maximum Entropy, & & \\  & & Support Vector Machines & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Noticeable researches connected to the application of ensemble learning to sentiment classification.