## 2 Literature Review  
Various scholars have expended a great deal of effort to construct models, datasets, and other technological resources for Roman Urdu/Hindi sentiment analysis. However, the difficulties and challenges of Roman Urdu/Hindi sentiment analysis have not yet been thoroughly examined. Many websites were crawled to construct a Roman Urdu corpus, which contains sentiments regarding various items and services, according to [24]. To classify the textual data, the authors used various machine learning methods such as NB, LR, and SVM. The study's findings showed that SVM outperformed other machine learning classifiers. In another investigation [25], the researchers gathered 300 negative and positive sentimentsfrom a blog in English and Roman Urdu. They then performed a sentiment analysis using three distinct machine learning models: KNN, NB, and decision tree. They discovered that NB outperformed the other two classifiers in terms of accuracy. [PERSON] and [PERSON] produced a corpus of user reviews in [26] by scraping several vehicle websites and categorizing them as negative or positive. The reviews were classified using random forest, multi-nominal NB, decision tree, KNN, and SVM machine learning classifiers. The experimental results showed that multi-nominal NB had the highest accuracy of all the algorithms examined. In a recent study [27], [PERSON] et al. created a Roman Urdu corpus with only 779 user reviews from five different genes, including movies, politics, mobile phones, miscellaneous, and theater. The researchers combined n-gram features with five machine learning models: LR, NB, KNN, SVM, and DT. Compared to the other machine learning algorithms, NB produced better outcomes (Table 1). Another study on Roman Urdu sentiment analysis [34] was conducted. The researchers used a hybrid model to examine Roman Urdu sentiment analysis using several lexicons and machine learning technologies. To categorize text data, they employed SVM and NB classifiers. Similarly, researchers presented a new feature representation approach for sentiment analysis in another study [35]. The performance of the suggested feature representation, dubbed \"Discriminative Feature Spamming\", was compared to binary weighting TF and TF-IDF, with character- and word-level n-gram features utilizing NB, LR, weighted voting, majority voting, and multi-layer perceptron models (MLP). The suggested feature representation method greatly improved the performance of all machine learning algorithms, according to the results. [PERSON] et al. [32] constructed a Roman Urdu dataset with reviews from six different genes in another research study. To address the extensive morphological structure of the Roman Urdu dialect, they suggested a methodology that utilized character-level, word-level, and a mix of word- and character-level features. They were able to improve the performance of machine learning classifiers by 12% above the baseline by doing so. On the other hand, deep learning classifiers such as recurrent neural network (RNN) and LSTM have shown promising results in a variety of NLP tasks. Another important study [31] tackled Roman Urdu sentiment analysis using a recurrent convolutional neural network (RCNN). The study's main contribution was to provide a state-of-the-art manually labeled corpus for the resource-scarce Roman Urdu language. The authors used a variety of models to categorize user evaluations, including rule-based, n-gram, and RCNN. There were two types of experiments: tertiary (neutral, negative, and positive) classifications and binary (negative and positive) classifications. The results revealed that RCNN was able to outperform the other tested approaches.