# Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
## 2 Literature Review
### Neural Network Models: A Review  
In the neural models, unique words are utilized as the input in the parse trees which provide the synthetic data and semantic data. Hence, emotion composition is derived from the best. Recurrent neural networks and convolution neural networks are becoming more popular, and they do not require parse trees to split their features from the given sentences. Instead, recurrent neural networks and convolution neural networks (CNNs) utilize word embedding as inputs which already inscribes the semantic and synthetic data. Additionally, the architecture of the convolution neural networks and recurrent neural networks help in learning the connectivity between the words in a statement. A recursive autoencoders network (RAN) in a semi-supervised model for sentence-level SA resulted in providing a low-dimensional vector representation [68]. In a new matrix-vector recursive neural network (MVRNN), each context is also related to a matrix representation in the form of a tree [69].  
The structure of a tree is derived from a parser that is used externally. It portrays a collaboration of Recurrent Neural Network (RNN) and CNN architecture for the classification of the sentiment from a short context which takes the favorable position in the coarse-grained features generated by a CNN [70].  
An approach based on the linguistics LSTM for effective sentiment prediction incorporates the sentiment lexicon as a highly intensive context and negative context [71]. The LSTM includes these features while analyzing the sentiment to provide a useful view of the context. Authors have presented a traditional CNN-LSTM model which consists of two sections--local CNN and LSTM--to predict the attitude illustrated in the content [72]. Table 5 presents the overall pros and cons identified from the survey.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & [60] & [61] & [62] & [63] & [64] & [65] & [66] & [67] \\ \hline Approach & Unsupervised & Supervised & Classifier approach & Classifier approach & Supervised & Unsupervised & Hybrid & Supervised \\ \hline Features & Sentiment orientation & Sentiment weight & Word step + sentiment n-gram & Word step + n-grams & N-gram sentiment weight & n-grams and SentiWord \\  & & & Analysis MSA & & & & & \\ \hline Algorithm & Based lexicon & Based lexicon & SVM, Naive Bayes (NB), KNN & SVM, NB, KNN & Rule-based and lexicon-based approach & SVM + lexicon-based approach & SVM, NB \\ \hline Dataset & Twitter & Social network & Aljazeera & Twitter and blogs & Twitter & Twitter & Microblog \\ \hline Accuracy & 83\% & 97\% & 96\% & 67\% & 75\% & 46\% & 84.01\% & 87\% \\ \hline Limitation & Negation and intensification & Manual extraction and feature score were & Few features as well & Could were & More classifiers can be included as well & More could be included & Negation was not & Usage of dictionary-based rule to translate the word \\  & considered & done & used & tag & rate & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: A chronological view of machine-learning-based sentiment analysis.