###### Abstract  
Sentiment analysis (SA) has been an active research subject in the domain of natural language processing due to its important functions in interpreting people's perspectives and drawing successful opinion-based judgments. On social media, Roman Urdu is one of the most extensively utilized dialects. Sentiment analysis of Roman Urdu is difficult due to its morphological complexities and varied dialects. The purpose of this paper is to evaluate the performance of various word embeddings for Roman Urdu and English dialects using the CNN-LSTM architecture with traditional machine learning classifiers. We introduce a novel deep learning architecture for Roman Urdu and English dialect SA based on two layers: LSTM for long-term dependency preservation and a one-layer CNN model for local feature extraction. To obtain the final classification, the feature maps learned by CNN and LSTM are fed to several machine learning classifiers. Various word embedding models support this concept. Extensive tests on four corpora show that the proposed model performs exceptionally well in Roman Urdu and English text sentiment classification, with an accuracy of 0.904, 0.841, 0.740, and 0.748 against MDPI, RUSA, RUSA-19, and UCL datasets, respectively. The results show that the SVM classifier and the Word2 Vec CBOW (Continuous Bag of Words) model are more beneficial options for Roman Urdu sentiment analysis, but that BERT word embedding, two-layer LSTM, and SVM as a classifier function are more suitable options for English language sentiment analysis. The suggested model outperforms existing well-known advanced models on relevant corpora, improving the accuracy by up to 5%. sentiment analysis; Roman Urdu language; LSTM; machine learning; deep learning; word embedding +
Footnote †: journal: Applied  
feelings behind user-shared comments in SA [14, 15]. Organizations and enterprises are interested in developing successful public relations strategies, running campaigns, overcoming weaknesses, and gaining more clients, as interest in SA grows. Businesses are eager to hear what customers have to say about their services and products [16, 17]. Furthermore, political parties are interested in learning about their popularity among the public and what the media has to say about them. In recent times, SA's focus has shifted to analyzing the emotions expressed in social media evaluations. The use of SA has expanded across a variety of spheres including harassment, politics, entertainment, sports, and medicine [18]. SA includes improved NLP approaches, data mining for predictive analysis, and contextual understanding of texts, all of which are current research issues [19, 20]. Machine learning approaches such as support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) have been used to solve diverse NLP tasks for many years. In several NLP-related applications, neural network (NN) methods created on dense vector representations have recently demonstrated state-of-the-art performances [21, 22]. Deep learning NNs initially demonstrated impressive performance in computer vision and pattern recognition workloads. Many deep learning algorithms have been employed to handle complex NLP problems such as sentiment analysis as a result of this trend. SA has received a great deal of research attention from academics. Although English and European languages have received considerable academic attention because they are considered rich dialects in terms of the tools and procedures needed to conduct SA, there are a number of other dialects, such as Roman Urdu/Hindi, that are considered to be resource deprived [23]. Urdu is the native and official language of Pakistan, and it is extensively spoken in many Indian states and Union territories. Roman Urdu and Romanaagari are names for the Urdu and Hindi languages written in the English alphabet, respectively. The majority of people in Pakistan, India, and other South Asian nations use Romanaagari or Roman Urdu script to interact on social media networks.