# Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu
## 1 Introduction  
Sentiment analysis is a branch of \"text classification\" and one of the eminent research areas in the field of natural language processing (NLP) [1]. Sentiment analysis can be performed at three levels, i.e., the aspect level, the sentence level, and the document level. Since the inception of social media and other user centric platforms, people began to share their reviews and sentiments against an entity, product, person, or an organisation [2]. Sentiment classification is also deliberated as automatically classifying text documents according to the predefined target polarity [3]. The polarity may be positive, negative, or neutral. Sentiment analysis is also defined as a branch of text classification, which is a field of study that analyses people's opinions, sentiments, appraisals, attitudes, and emotions toward entities and their attributes expressed in written text. Sentiment analysis has been found to be a powerful tool for businesses, governments, and researchers to extract and analyse public mood and views, to gain business insight, and to make better decisions [4, 5]. In E-commerce, decision makers are always interested in obtaining feedback from a user so as to trigger the right decision at the right time. In Pakistan and its sub-continent, people mostly express their reviews in Roman Urdu, which is overwhelmingly increasing on the web. Roman Urdu is basically an amalgamation of English alphabets and Urdu. In other words, English alphabets are used to express Urdu, creating Roman Urdu. For example, the word \"Justice\" in Roman Urdu is equivalent to \"Insaf\". The role of Roman Urdu in multilingual information retrieval was investigated in a regional study, and it was revealed that Internet users in Pakistan mostly prefer Roman Urdu over conventional nastaliq Urdu script [6]. In the next section, the structure of Roman Urdu is discussed in detail. The astronomical rise in the amount of online data regarding product services, events, issues, and celebrities has triggered the need to find an automated way to comprehend user reviews. Sentiment analysis, a dedicated task, has emerged as a promising area of NLP in this regard. One of the most compelling reasons to use sentiment analysis widely is that it greatly aids businesses in understanding consumer needs and in formulating critical modifications in marketing and business strategies to improve user experience [7; 8; 9]. For data analysts, it is almost impossible to comprehend the amount of data manually. E-commerce systems, if powered by the state-of-the-art sentiment analysis, may yield excellent results. Moreover, sentiment analysis also helps assess other systems such as recommendation systems, hate speech detection, and spam detection, among others. In E-commerce, user reviews play vital roles in decision-making so as to ameliorate business practices. These reviews may include the utility, pricing, promotion, unique features, discounting, delivery, service, organisation, individual, or any other aspect of the experience process with the item. Interpreting the sentiments of the users refers to sentiment analysis and is one of the most active research area in NLP [10]. Contemporary advancements in sentiment analysis approaches based on machine learning and deep learning have greatly improved the performance of business intelligence [11], and scientific and academic applications. During the last few years, the E-commerce sector in Pakistan has grown at a great pace in which the leading E-commerce giant Daraz.pk has played a vital role. The masses in Pakistan and its sub-continent mostly express their reviews/sentiments in Roman Urdu. Owing to the colloquial nature of this text, it is very much challenging to perform sentiment classification. Moreover, there exists no grammar or spelling rules for Roman Urdu, and for a single word, there exists a number of variants, which makes it very much informal. However, in this region, this informal text is the first choice used to express the sentiments of the user.