A hybrid dependency-based approach for Urdu sentiment analysis

• Published in: Scientific reports Vol. 13; no. 1; p. 22075
• Main Authors: Sehar, Urooba, Kanwal, Summrina, Allheeib, Nasser I., Almari, Sultan, Khan, Faiza, Dashtipur, Kia, Gogate, Mandar, Khashan, Osama A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.12.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 639/705/258; Datasets; Humanities and Social Sciences; Language; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Sentiment analysis; Social networks; Urdu language
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-48817-8

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.