# Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
## 3 Methodology
### Machine Learning Approaches  
As discussed in the literature review, SA can be performed through various methods. Figure 4 shows the categorization of the methods in the analysis of sentiment and aggregation of opinions. Figure 6 mentions the different approaches that are discussed here. The methods are a probabilistic classifier, linear classifier, rule-based classifier, DT classifier, and NB classifier. The probabilistic classifier (PC) provides a probabilistic function to the set of input data. Further, the input function f(x) is applied with the probability and is mapped with the output function (y). Hence, the PC is denoted as follows:  
\[\text{Y}^{\prime}=\text{f(x)} \tag{3}\]  
where f(x) is the input function. When the conditional distributors replace the PC, it is as follows:  
\[\text{Z}^{\prime}=\text{arg max}\;\text{Z}\;\text{Pr}\;(\text{Z}^{\prime}=\text {z/V}^{\prime}) \tag{4}\]  
where the PC is changed to the conditional classifiers \(\text{Pr}(\text{Z}^{\prime}/\text{V}^{\prime})\), and the given z Z\({}^{\prime}\) is assigned to v V\({}^{\prime}\). In a linear classifier, word T = (T1, T2, T3) is the frequency of a single word, the vector V = (V1, V2, V3) is a linear input coefficient, and the scalar S is the linear output coefficient. This classifier categorizes the margin to distinguish between the two classes [73]. In the rule-based classifier, the \"if condition and then decision\" approach is used to make specific rules. This rule-based classifier is also known as a multi-class classifier. Furthermore, this classifies the data into three forms--good, bad, and neither good nor bad. Besides, this unsupervised classifier mainly focuses on the prediction of emotion in the context and emoticons [74]. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Pros** & **Cons** \\ \hline \(\bullet\) & Many utilized fewer parameters. & \(\bullet\) & Some of the approaches require labeled data. \\ \hline \(\bullet\) & Labeling data is not necessary for feature & \(\bullet\) & It does not count the absence of text. \\ extraction. & \(\bullet\) & Features are confused at times during analysis. \\ \hline \(\bullet\) & Some of the low-featured data can be extracted & \(\bullet\) & When there is more than one possible opinion, \\ easily. & & it fails to handle the situation. \\ \hline \(\bullet\) & Opinion words are categorized into different forms. & \(\bullet\) & Many consider only adjectives. \\ \hline \(\bullet\) & Develops new standardized data. & \(\bullet\) & Exact feature identification is complicated. \\ \hline \(\bullet\) & Implicit statements are considered in the text & \(\bullet\) & Unlabeled data are not analyzed properly. \\ \hline \(\bullet\) & Some of the lexicon approaches provide less false & \(\bullet\) & It does not analyze short text properly. \\ \hline \(\bullet\) & Domain-specific data achieve high accuracy in predicting sentiments. & \(\bullet\) & Even though it ensures high accuracy, F-measure is also high. \\ \hline \(\bullet\) & Neural net models on predicting the sentiment & \(\bullet\) & Building neural net ID is highly complicated. \\ achieve high accuracy with a less false rate. & \(\bullet\) & The false rate in the analysis is high in ontology \\ \hline \(\bullet\) & Ontology sentiment analysis provides high accuracy when it is domain-specific. & when compared to the lexicon. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Advantages and disadvantages of existing research work on sentiment analysis. The DT classifier is a recursive one. For the training data, the condition based on the classification is applied, and it divides the training data such that the ones which satisfy the conditions are all of one class, and the procedure continues until all the data satisfy the condition [75]. The NB approach examines whether the feature provided with the probability has the role of the label or not. In this, every feature is independent and the specific feature is mapped with a label matched in the maximum.