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

Altaf, Amna; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Bajwa, Usama Ijaz

doi:10.1007/s11042-023-15216-0

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…In their research, Altaf et al 42 employed linguistic variables that are unique to the Urdu language to analyze sentiment at the sentence level. Furthermore, conventional machine learning methodologies were utilized in order to categorize idioms and proverbs.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid dependency-based approach for Urdu sentiment analysis

Sehar,

Kanwal,

Allheeib

et al. 2023

Sci Rep

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In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of topics. Businesses can leverage this data to extract valuable insights, improve their services, and effectively reach a broader audience based on users’ expressed opinions on social media platforms. To harness the potential of this extensive and unstructured data, a deep understanding of Natural Language Processing (NLP) is crucial. Existing approaches for sentiment analysis (SA) often rely on word co-occurrence frequencies, which prove inefficient in practical scenarios. Identifying this research gap, this paper presents a framework for concept-level sentiment analysis, aiming to enhance the accuracy of sentiment analysis (SA). A comprehensive Urdu language dataset was constructed by collecting data from YouTube, consisting of various talks and reviews on topics such as movies, politics, and commercial products. The dataset was further enriched by incorporating language rules and Deep Neural Networks (DNN) to optimize polarity detection. For sentiment analysis, the proposed framework employs predefined rules to trigger sentiment flow from words to concepts, leveraging the dependency relations among different words in a sentence based on Urdu language grammatical rules. In cases where predefined patterns are not triggered, the framework seamlessly switches to its sub-symbolic counterpart, passing the data to the DNN for sentence classification. Experimental results demonstrate that the proposed framework surpasses state-of-the-art approaches, including LSTM, CNN, SVM, LR, and MLP, achieving an improvement of 6–7% on Urdu dataset. In conclusion, this research paper introduces a novel framework for concept-level sentiment analysis of Urdu language data sourced from social media platforms. By combining language rules and DNN, the proposed framework demonstrates superior performance compared to existing methodologies, showcasing its effectiveness in accurately analyzing sentiment in Urdu text data.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid dependency-based approach for Urdu sentiment analysis

Sehar,

Kanwal,

Allheeib

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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“…Nasib et al [40] in 2018 obtained good accuracy results converting natural Bengali language to text with an adequately trained model based on an open-source text-to-speech Java framework. A very recent work by Altaf et al [41] exploits linguistic features of Urdu language (a major Pakistani language) for sentence-level SA and classifies idioms and proverbs using classical machine learning techniques. The above approaches use either machine learning or deep learning; accordingly, they need an ample training set and relevant computational resources [11].…”

Section: Related Workmentioning

confidence: 99%

Revealing People’s Sentiment in Natural Italian Language Sentences

Calvagna,

Tramontana,

Verga

2023

Computers

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Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.

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

Cited by 5 publications

References 34 publications

A hybrid dependency-based approach for Urdu sentiment analysis

A hybrid dependency-based approach for Urdu sentiment analysis

Revealing People’s Sentiment in Natural Italian Language Sentences

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

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