Deep-learning based framework for sentiment analysis in Urdu language

Masood, Maria; Azam, Farooque; Anwar, Muhammad; Rahman, Jalees Ur

doi:10.1109/icodt255437.2022.9787451

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Chandio et al [47] created RU-BiLSTM with attention mechanism and embeddings to analyze sentiment in R-Urdu. Masood et al [48] developed a deep learningbased LSTM architecture with an 830-word manual dictionary for stemming in Urdu and achieved an accuracy rate of 86.8% and F1-Score of 89%. Qureshi et al [25] compared nine machine learning algorithms using a dataset named R-Urdu (DRU) and found that LR performed better than others with an accuracy of 92.25% for binary classification.…”

Section: Deep Learning Based Sentiment Analysis Of Urdu Textmentioning

confidence: 99%

BERT-Based Sentiment Analysis for Low-Resourced Languages: A Case Study of Urdu Language

Ashraf,

Jana,

Umer

et al. 2023

IEEE Access

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Sentiment analysis holds significant importance in research projects by providing valuable insights into public opinions. However, the majority of sentiment analysis studies focus on the English language, leaving a gap in research for other low-resourced languages or regional languages, e.g., Persian, Pashto, and Urdu. Moreover, computational linguists face the challenge of developing lexical resources for these languages. In light of this, this paper presents a deep learningbased approach for Urdu Text Sentiment Analysis (USA-BERT), leveraging Bidirectional Encoder Representations from Transformers and introduces an Urdu Dataset for Sentiment Analysis-23 (UDSA-23). USA-BERT first preprocesses the Urdu reviews by exploiting BERT-Tokenizer. Second, it creates BERT embeddings for each Urdu review. Third, given the BERT embeddings, it fine-tunes a deep learning classifier (BERT). Finally, it employs the Pareto principle on two datasets (the state-of-the-art (UCSA-21) and UDSA-23) to assess USA-BERT. The assessment results demonstrate that USA-BERT significantly surpasses the existing methods by improving the accuracy and f-measure up to 26.09% and 25.87%, respectively.

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Section: Deep Learning Based Sentiment Analysis Of Urdu Textmentioning

confidence: 99%

BERT-Based Sentiment Analysis for Low-Resourced Languages: A Case Study of Urdu Language

Ashraf,

Jana,

Umer

et al. 2023

IEEE Access

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“…LSTM was applied for sentiment analysis and results were obtained. [6]. Sentiments from social data were analyzed using Urdu pre-existing algorithms, attaining adequate results [7].…”

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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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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Exploiting Linguistic Features for Effective Sentence-Level Sentiment Analysis in Urdu Language

Altaf

Anwar

Jamal

et al. 2023

Multimed Tools Appl

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Deep-learning based framework for sentiment analysis in Urdu language

Cited by 6 publications

References 19 publications

BERT-Based Sentiment Analysis for Low-Resourced Languages: A Case Study of Urdu Language

BERT-Based Sentiment Analysis for Low-Resourced Languages: A Case Study of Urdu Language

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

Exploiting Linguistic Features for Effective Sentence-Level Sentiment Analysis in Urdu Language

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