# Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu
## 2 Related Work
### An Insight into Roman Urdu Sentiment Analysis  
In a systematic literature review (SLR) on Roman Urdu sentiment analysis, it was identified that the Urdu language lacks standardised datasets for sentiment analysis, which is critical [49]. Furthermore, pre-processing and feature selection mechanisms should be designed in accordance with Urdu language's peculiarities. In a recent study on lexical normalisation on Roman Urdu, a phonetic algorithm-based approach TERUN (Transliteration based Encoding for Roman Urdu text Normalisation) was proposed [50]. TERUN uses features of Roman Hindi/Urdu linguistics to convert lexically variable terms to their canonical forms. Transliteration-based encoder, filter module, and hash code ranker are the three interconnected modules. For a single Roman Hindi/Urdu word, the encoder creates all possible hash-codes. The third module ranks the filtered hash-codes depending on their relevancy, while the fourth component filters the irrelevant codes. The goal of this research is not only to normalise the text but also to see how it affects text classification. Recently, a novel term-weighting technique was introduced known as discriminative feature spamming method (DFST), which is a unique word weighting technique for Roman Urdu sentiment analysis that selects distinctive terms based on a term utility criteria (TUC) and then spams them to improve their discriminative strength [28]. The experiments were conducted on 11,000 Roman Urdu reviews and a custom tokeniser was built to improve the accuracy. In an earlier study [PERSON] et al. [51] collected Roman Urdu product reviews for sentiment analysis. They experimented with three machine learning schemes: Naive Bayes, SVM, and Logistic Regression. They revealed that SVM outperforms the rest of the classifiers. Likewise, [PERSON] et al. [13] also proposed SVM for this task. They collected 20,000 product reviews from Daraz.pk and experimented with different SVM kernels. An accuracy of 60% was reported under linear SVM kernel and bag-of-word features. In 2016, [PERSON] et al. [52] experimented with a very small set of Roman Urdu reviews containing 150 positive and 150 negative reviews. They employed three machine learning classifiers including Naive Bayes, Decision Tree, and KNN using WEKA tool. It was identified that Naive Bayes showed a better performance than Decision Tree and KNN in terms of accuracy, precision, recall, and F-measure. In another attempt to perform a Roman Urdu sentiment analysis, reviews were collected from different automobile websites for a binary classification task. [PERSON] et al. [53] empirically evaluated ten different classifiers: SVM, KNN, Decision Tree, Passive Aggressive, Ensemble classifier, Perceptual, SSGD, Naive Bayes, Ridge classifier, and nearest centroid. In their experimental settings, they employed three different feature representation schemes i.e., TF, TF-IDF, and Hashing vectoriess, and three feature selection schemes, i.e., Chi-Squared, IG, and MI. Their empirical evaluation of diverse machine learning classifiers determined that SVM outperforms the rest of the classifiers. [PERSON] et al. [54] conducted a study using Naive Bayes, Logistic Regression, LSTM, and CNN on Roman Urdu News categorisation. The goal of the research was to categorise the news into five groups (health, business, technology, sports, and international). For lexical normalisation and noise reduction in the corpus, they used a phonetic algorithm. Naive Bayes beats the other classifiers, according to the findings. [PERSON] et al. [55] has recently experimented with a bunch of machine learning algorithms for the sentiment analysis of Roman Urdu and with their proposed SVM. Some researchers have also utilised deep neural networks for the sentiment classification of Roman Urdu. [PERSON] et al. [12] created a new Roman Urdu corpus RUSA-19 containing 10,012 reviews.