A Review of Urdu Sentiment Analysis with Multilingual Perspective: A Case of Urdu and Roman Urdu Language

Khan, Ihsan Ullah; Khan, Amin; Khan, Wahab; Su’ud, Mazliham Mohd; Alam, Muhammad Mansoor; Subhan, Fazlı; Asghar, Muhammad Zubair

doi:10.3390/computers11010003

Cited by 28 publications

(27 citation statements)

References 37 publications

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“…The values of this feature are given by the lexical word at the xand y-coordinates, where the values of x and y vary between −1 and 1. Formally, it is defined in Equation (6).…”

Section: Feature Templatesmentioning

confidence: 99%

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Named Entity Recognition Using Conditional Random Fields

et al. 2022

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Named entity recognition (NER) is an important task in natural language processing, as it is widely featured as a key information extraction sub-task with numerous application areas. A plethora of attempts was made for NER detection in Western and Asian languages. However, little effort has been made to develop techniques for the Urdu language, which is a prominent South Asian language with hundreds of millions of speakers across the globe. NER in Urdu is considered a hard problem owing to several reasons, including the paucity of large, annotated datasets; an inaccurate tokenizer; and the absence of capitalization in the Urdu language. To this end, this study proposed a conditional-random-field-based technique with both language-dependent and language-independent features, such as part-of-speech tags and context windows of words, respectively. As a second contribution, we developed an Urdu NER dataset (UNER-I) in which a large number of NE types were manually annotated. To evaluate the effectiveness of the proposed approach, as well as the usefulness of the dataset, experiments were performed using the dataset we developed and an existing dataset. The results of the experiments showed that our proposed technique outperformed the baseline technique for both datasets by improving the F1 scores by 1.5% to 3%. Furthermore, the results demonstrated that the enhanced dataset was useful for learning and prediction in a supervised learning approach.

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“…The values of this feature are given by the lexical word at the xand y-coordinates, where the values of x and y vary between −1 and 1. Formally, it is defined in Equation (6).…”

Section: Feature Templatesmentioning

confidence: 99%

“…In morphologically rich languages, the number of words created from a single root word is often considerable. Furthermore, In comparison to NER for other languages, the research in this field is substantially smaller, and the accessible facilities are minimal [5,6]. UNER researchers primarily used three methodologies.…”

Section: Introductionmentioning

confidence: 99%

Named Entity Recognition Using Conditional Random Fields

et al. 2022

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“…• CC benefits customers by allowing [7] cloud [76] providers to use infrastructure, networks, and applications on-demand, at a low cost, and elastically [7]. • MCC provides mobile users with cloud [35] data [65] storage [35] and processing [86] capabilities, removing the need for a powerful computer setup [7] (for example, CPU speed [9], memory space [9], and others.) since all resource-intensive CC can be done [7].…”

Section: A Why Mobile Cloud Computingmentioning

confidence: 99%

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Shahid¹,

Faiz²,

Alam³

et al. 2022

JISR-C

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Cloud computing (CC) provides fast and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is a new technology that brings CC and mobile devices together. MCC allows mobile devices access to cloud services. Describe the basic architectures for various MCC applications, as well as a brief comparison of cloud and MCC. As CC becomes more common, researchers in this field may need to conduct real-world experiments in their studies. Configuring and running these experiments in real-world cloud environments is costly. As a consequence, modeling and simulation approaches are suitable solutions that can be used to simulate cloud computing environments. Several simulation tools tailored to CC have been developed. The most powerful simulation methods in this field of study are described in this article. Among them are CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports.

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“…As per existing research studies, SA can be categorized as multi-domain SA [5], [7], [8], Cross Domain Sentiment Analysis (CDSA) [9], [10], bilingual SA [11], [12], and multilingual SA [13], [14]. In multi-domain SA, the dataset is collected from multiple genres, and training and testing of models are performed on the same dataset.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Altaf

Jamal

et al. 2022

IEEE Access

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Sentiment analysis is a widely researched area due to its various applications in customer services, brand monitoring, and market research. Automatic sentiment classification is an important but challenging task. Contrary to the English language, sentiment analysis for low-resource languages like Urdu is an under-explored research area. Most of the work on sentiment analysis in the Urdu language is domain-dependent where models are mostly trained and tested on the same dataset on limited domains. However, sentiments in different domains are expressed differently, and manually annotating the datasets for all possible domains is unfeasible. Training a sentiment classifier using annotated data on one domain and testing it on another domain results in poor performance as the terms appearing in the source domain (training data) might not appear in the target (testing data) domain. In this paper, we present a baseline method for cross-domain sentiment analysis in the Urdu language using two different domains. Feature extraction is performed using n-grams and word embedding techniques. Sentiment classification is performed using machine learning and deep learning classifiers. The proposed method achieves an accuracy, precision, recall, and F1 scores of 0.77, 0.83, 0.68, and 0.75, respectively.INDEX TERMS Cross-domain sentiment analysis; deep learning; Urdu language processing; feature engineering

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A Review of Urdu Sentiment Analysis with Multilingual Perspective: A Case of Urdu and Roman Urdu Language

Cited by 28 publications

References 37 publications

Named Entity Recognition Using Conditional Random Fields

Named Entity Recognition Using Conditional Random Fields

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

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