An Intelligent Unsupervised Approach for Handling Context-Dependent Words in Urdu Sentiment Analysis

Mukhtar, Neelam; Khan, Masood Alam; Chiragh, Nadia; Nazir, Shah; Jan, Asim Ullah

doi:10.1145/3510830

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Urdu language data were collected and some pre-processing was applied using classical machine learning along with deep learning techniques to perform sentiment analysis [8]. The characteristic of context dependency of Urdu words was resolved to conduct Urdu sentiment analysis by assigning polarities to sentences; the accuracy of results therefore improved [9].…”

Section: Relation To Previous Workmentioning

confidence: 99%

“…Sentiment analysis in Urdu was performed using a lexicon-based approach, a traditional method adopted using rules and results obtained in Urdu [9]. Multimodal datasets concerning text, audio, and videos of Urdu and comprising 1372 expressions were collected to identify context-aware sentiments, achieving a reasonable accuracy [10].…”

Section: Relation To Previous Workmentioning

confidence: 99%

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Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets

Muhammad

Burney

2023

Symmetry

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Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis.

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Section: Relation To Previous Workmentioning

confidence: 99%

Section: Relation To Previous Workmentioning

confidence: 99%

Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets

Muhammad

Burney

2023

Symmetry

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“…Mukhtar et al [22] perform SA in Urdu language using a lexicon-based approach by enhancing an existing SA lexicon and introducing context-dependent words in it. These words are used with or without conjunctions.…”

Section: A Lexicon-based Approachmentioning

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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Comprehensive review and comparative analysis of transformer models in sentiment analysis

Bashiri,

Naderi

2024

Knowl Inf Syst

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An Intelligent Unsupervised Approach for Handling Context-Dependent Words in Urdu Sentiment Analysis

Cited by 6 publications

References 29 publications

Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets

Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Comprehensive review and comparative analysis of transformer models in sentiment analysis

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