# Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu
## 2 Related Work
It was identified that deep learning classifiers such as CNN, RNN, and LSTM embedded within a sentence attention framework yields good results. [PERSON] et al. [36] has proposed Bidirectional LSTM for Arabic sentiment analyses. Arabic being a morphologically rich and complex language has presented challenges for researchers to undertake sentiment analyses. The experiments were conducted on six benchmark Arabic datasets, which revealed that the proposed model outperformed the state-of-the art machine learning and deep learning models. In a study conducting a sentiment analysis of Chinese textual data, it was identified that sentence semantics play significant roles [37]. To assess the sentiment tendency of Chinese texts, this research offered a scalable multi-channel dilated combined architecture of the convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) model with an attention mechanism. The study was aimed at exploiting both the original context features and high level features using multi-scale dilated architecture. In contrast to deep learning, some researchers have employed lexical knowledge graph-based techniques. The majority of recent Lexical sentiment analysis (LSA) methods have relied on supervised learning techniques applied to corpus-based statistics, which need a lot of training data, a lot of time, and a lot of statistical corpora. Other research have used unsupervised and lexicon-based algorithms to match target words in a lexical knowledge base (KB) with seed words in a sentiment lexicon but have run into issues with affective concept coverage and connectedness. [PERSON] et al. [38] has introduced an LSA-based unsupervised approach LISA, a knowledge graph-based LSA framework that is unsupervised at the word level. It computes and propagates affective scores in a lexical-affective graph (LAG) formed by linking a conventional lexical KB such as WordNet with a trustworthy affect KB such as WordNet-Affect Hierarchy using different variations of shortest path graph navigation algorithms. Sequence-to-sequence deep learning models are a class of Recurrent Neural Networks (RNNs) that are used for different language problems such as machine translation, text summarisation, question answering, and text generation. Since their inception, deep generative models saw a boom in popularity among deep learning researchers. These models are used to generate new synthetic data such as photos, movies, and texts by fitting approximate distributions to the data [39]. The generative models are also utilised for drug discovery and molecular design. Several ways of developing algorithmic music composers have been explored in recent years. The majority of current methods focus on making music that looks to be technically right or listener-friendly and captivating. However, few approaches have been developed to create music that communicates human emotions based on sentiments. [PERSON] et al. [40] has introduced an algorithmic approach for autonomous music sentiment-based expression and composition, namely MUSEC, that may understand an extensible set of six primary human emotions (e.g., anger, fear, joy, love, sadness, and surprise) expressed by a MIDI musical file and then composes (creates) new, polyphonic (pseudo) thematic and diversified musical pieces that express these emotions. Some researchers have also employed generative models for sentiment detection of stock messages of investors. The generative models converts the messages to emotion vectors during the training process. Subsequently, these emotion vectors are fed to the classifier. Their experimental results reveal that generative models are effective for short-messages sentiment classification. [PERSON] et al. [41] has utilised a semi-supervised approach for the sentiment analysis of stock messages of investors. Aspect-level sentiment analysis is a fine-grained sentiment analysis technique that aims to determine the sentiment polarity of a given opinion target conveyed.