A Hybrid Machine Learning Model to Predict Sentiment Analysis on X

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ID: 313043
2024
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Abstract
Social media, particularly Twitter now ????, have emerged as pivotal arenas for sentiment analysis due to their pervasive nature and significant impact on shaping opinions. Our research delves into Roman-Urdu sentiment analysis within the burgeoning realm of social media, addressing a significant gap in research. Leveraging machine learning techniques, it emphasizes the scarcity of sentiment analysis studies in this linguistic domain, specifically on platforms like Twitter. The methodology involves meticulous data collection from English and Roman-Urdu tweets, followed by comprehensive preprocessing to refine and enhance dataset quality in python. Feature extraction retrieves key characteristics like subjectivity and polarity, enabling a nuanced sentiment analysis. Our technique evaluates precision, don't forget, F1 rating, and accuracy metrics the use of a complete evaluation framework on 4 machine learning classifiers: Naïve Bayes (NB), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) algorithms. Roman Urdu sentiment analysis has advanced way to the results, which show how nicely this method works to classify all three sentiments (Hate, Offensive, and Neither) in multilingual social media content.
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imported_1777058643_69ebc3535d268 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fiza Malik, Muhammad Fuzail, Naeem Aslam, Ramla Sarwar, Kamran Abid, Muhammad Sajid Maqbool, Anum Yousaf
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
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URL
https://jcbi.org/index.php/Main/article/view/305
Keywords Keywords not found

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