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

Muhammad, Khalid Bin; Burney, S. M. Aqil

doi:10.3390/sym15051027

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…Mahmood et al [28] presented a deep learning model to extract emotions and attitudes of people expressed in R-Urdu, where RCNN outperformed baseline approaches with accuracy scores of 0.652% and 0.572% for binary and ternary classifications, respectively. K. B. Muhammad and S. M. Aqil Burney [50] applied machine and deep learning algorithms for Urdu text Classification of symmetric datasets. They found that machine learning algorithms showed good results, while most deep learning algorithms improved the results further.…”

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

View full text Add to dashboard Cite

show abstract

“…Yadav andVishwakarma 2020, Saberi andSaad 2017), is now becoming popular in other low resource languages like Urdu (e.g. Noor et al 2019, Muhammad andBurney 2023), Pashto (e.g. Iqbal et al 2022, Kamal et al, Kamal et al), Bangla (e.g.…”

Section: Related Workmentioning

confidence: 99%

nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis

Goswami,

Raihan,

Puspo

et al. 2023

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

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In this paper, we discuss the nlpBDpatriots entry to the shared task on Sentiment Analysis of Bangla Social Media Posts organized at the first workshop on Bangla Language Processing (BLP) co-located with EMNLP. The main objective of this task is to identify the polarity of social media content using a Bangla dataset annotated with positive, neutral, and negative labels provided by the shared task organizers. Our best system for this task is a transfer learning approach with data augmentation which achieved a micro F1 score of 0.71. Our best system ranked 12 th among 30 teams that participated in the competition.

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Augmenting sentiment prediction capabilities for code-mixed tweets with multilingual transformers

Hashmi,

Yayilgan,

Shaikh

2024

Soc. Netw. Anal. Min.

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People in the modern digital era are increasingly embracing social media platforms to express their concerns and emotions in the form of reviews or comments. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. Currently, individuals tend to express their thoughts in their native languages on these platforms, which is quite challenging due to potential syntactic ambiguity in these languages. Most of the research has been conducted for resource-aware languages like English. However, low-resource languages such as Urdu, Arabic, and Hindi present challenges due to limited linguistic resources, making information extraction labor-intensive. This study concentrates on code-mixed languages, including three types of text: English, Roman Urdu, and their combination. This study introduces robust transformer-based algorithms to enhance sentiment prediction in code-mixed text, which is a combination of Roman Urdu and English in the same context. Unlike conventional deep learning-based models, transformers are adept at handling syntactic ambiguity, facilitating the interpretation of semantics across various languages. We used state-of-the-art transformer-based models like Electra, code-mixed BERT (cm-BERT), and Multilingual Bidirectional and Auto-Regressive Transformers (mBART) to address sentiment prediction challenges in code-mixed tweets. Furthermore, results reveal that mBART outperformed the Electra and cm-BERT models for sentiment prediction in code-mixed text with an overall F1-score of 0.73. In addition to this, we also perform topic modeling to uncover shared characteristics within the corpus and reveal patterns and commonalities across different classes.

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

Cited by 8 publications

References 38 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

nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis

Augmenting sentiment prediction capabilities for code-mixed tweets with multilingual transformers

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