## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Used Corpus** & **Classification Algorithms** & **Learning Method** & **Embedding and Features** & **Accuracy (\%)** \\ \hline
[28] & 15,000 user reviews gathered from various websites & Neural Network & Supervised & - & 80 \\ \hline
[29] & Only 300 reviews of positive and negative classes & NB, DT, KNN & Supervised & - & 71 \\ \hline
[30] & 3241 sentences of positive, negative, and neutral classes & SVM, LR, NB, RCNN, Hybrid Multichannel Approach & Supervised and FastText & Word2 Vec, GloVe and FastText & 82 \\ \hline
[31] & 10,021 user sentences from various websites of three classes & RCNN, Rule-based and N-gram & Supervised and Unsupervised & Word2 Vec & 75 \\ \hline
[32] & 11,000 reviews of two classes & KNN, DT, RF, LR, NB, ANN, SVM, AdaBoost, wVoting & Supervised & - & 82 \\ \hline
[33] & 11,000 sentences of two classes & KNN, DT, RF, LR, NB, ANN, SVM, AdaBoost, wVoting & Supervised & - & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary and comparison of existing literature. The most important work [36] has recently been done by employing various machine learning and deep learning techniques for SA of Urdu text. Firstly, user reviews in Urdu from six different domains were gathered from various social media platforms to create a state-of-the-art corpus. Human specialists later carefully annotated the entire Urdu corpus. Finally, the created Urdu corpus was validated using a combination of machine learning techniques such as RF, NB, SVM, AdaBoost, MLP, and LR, and deep learning algorithms such as LSTM and CNN-1D. LR algorithms outperformed all other machine learning and deep learning algorithms in terms of accuracy. In [30], another attempt was made to solve the problem of Roman Urdu sentiment analysis using supervised machine learning and deep learning models with word embedding. The authors gathered 3241 Roman Urdu positive, negative, and neutral feelings. SVM, LR, and NB were among the machine learning algorithms used. The authors also evaluated their corpus using a hybrid multi-channel method, which included testing deep learning techniques such as RCNN and RNN. They used three neural word embedding approaches in their suggested hybrid approach: Word2 Vec, GloVe, and FastText. In terms of accuracy, F1 score, precision, and recall, their suggested hybrid multi-channel framework outperformed the other applied machine and deep learning methods by a wide margin. Similarly, LSTM with a word embedding layer was used to test another [27] deep-learning-based model for Roman Urdu sentiment analysis. The input layer, word embedding layer, LSTM layer, and final output layer were the four layers of the suggested methodology. The proposed model outperformed the competition in terms of accuracy. Table 1 represents a short summary of the literature. Some examples of to neutral, positive, and negative classes of Roman Urdu/Hindi text are presented in Figure 1.  
In [37], the authors analyze several categorical ways to leverage normative databases as a way of processing text with a dimensional model for the categorical models. Three dimensionality reduction strategies were evaluated: latent semantic analysis (LSA), probabilistic latent semantic analysis (PLSA), and non-negative matrix factorization (NMF). A normative database was used to create three-dimensional vectors (valence, arousal, dominance). The results revealed that dimensional modeling and the NMF categorical model performed the best. In another study [38] EmoBank discriminates between writer and reader emotions, whereas a subset of the corpus uses categorical VAD annotations based on basic emotions. In [39], the authors use Twitter data to understand the method for conveying the message in a different language, and they also analyze systems for bias towards specific races or genders.