# Roman Urdu Sentiment Analysis Using Transfer Learning
## 2 Literature Review
### Roman Urdu Sentiment Analysis  
In a recent lexical normalization study for Roman Urdu [55], a phonetic technique named TERUN (Transliteration-based Encoding for Roman Urdu text Normalization) was proposed. To standardize lexically flexible terminology, TERUN utilizes Roman Hindi/Urdu linguistics. An encoder based on transliteration, a filter module, and a hash code ranker is the three interconnected components. In the encoder, a single word in Romanized Hindi or Urdu will create every hash code. The filtered hash codes are prioritized by relevancy in the third module, and the irrelevant codes are dismissed in the fourth. The purpose of this study is twofold: to standardize and categorize text. For Roman Urdu sentiment analysis, a new term-weighting method termed discriminative feature spamming strategy (DFST) was recently presented [56]. DFST works well for sentiment analysis because it uses a term utility criterion (TUC) to pick out different words and then spams them to make them more distinctive. Experiments were run on 11,000 Roman Urdu reviews, and a specialized tokenizer was built to improve accuracy. [PERSON] et al. [57] examined Roman-Urdu product reviews for SA. They experimented with Naive Bayes, Support Vector Machines, and Logistic Regression, all of which are machine learning methods. It was clear from the outcomes that SVM is a better classifier than the others. [PERSON] et al. [58] have suggested SVM for this service. They experimented with several SVM kernels after collecting 20,000 customer reviews of products available on \"Daraz.pk\". The bag-of-word features and linear SVM kernel are relied upon to arrive at the claimed 60% accuracy. A small selection of Roman Urdu reviews, including only 150 positive and 150 negative ratings, was used by [PERSON] et al. [59] to test their theories in 2016. The KNN, DT, and NB classifiers for machine learning were all implemented in WEKA. For all four metrics (accuracy, precision, recall, and F-measure), NB was deemed to be the clear winner over DTand KNN. In an extra effort to perform Roman Urdu SA, reviews from various websites were collected for a binary classification assignment. Ten alternative classifiers (SVM, KNN, Decision Tree, Passive Aggressive, Ensemble classifier, Perceptron, SSGD, Naive Bayes, Ridge classifier, and nearest centroid) were compared in an empirical study by [PERSON] et al. [60]. They conducted experiments using Chi-squared, IG, and MI feature selection algorithms and three different feature representation systems (TF, TF-IDF, and Hashing vectorizer). Support vector machines fared best in an empirical comparison of several ML classifiers undertaken by the authors. [PERSON] et al. [61] looked at the issue of categorizing Roman Urdu news using various methods, including Naive Bayes, Logistic Regression, Long Short-Term Memory, and Convolutional Neural Networks. The purpose of the research was to divide the news into five separate types (health, business, technology, sports, and international). When standardizing the vocabulary and cleaning up the corpus, they used a phonetic algorithm. According to the findings, Naive Bayes is the most effective classifier. Recently, [PERSON] et al. [62] have compared the effectiveness of their proposed SVM to that of other ML algorithms for evaluating Roman Urdu sentiment. Researchers have used deep neural networks to classify sentiments in Roman Urdu texts. With the support of other scholars, [PERSON] et al. [63] have constructed a new Roman Urdu corpus RUSA-19 that includes 10,012 reviews. The ratings come from different social media sites, and they span six categories: (i) media (television, film, and radio); (ii) food; (iii) politics and policy; (iv) mobile technology; and (v) sports. They evaluated a new dataset and proposed an RCNN-based deep network for sentiment analysis, and the proposed model was constructed using both recurrent and convolutional neural network (CNN) layers. However, numerous studies have pointed out that CNN does better with non-sequential data than with sequential data.