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Register a new userTowards Sentiment Analysis of Tobacco Products' Usage in Social Media
نحو تحليل المعرفات لاستخدام منتجات التبغ في وسائل التواصل الاجتماعي
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تشعر الدراسات المرتبطة بالتبغ المعاصرة في الغالب بمنصة وسائط اجتماعية واحدة أثناء تفويتها على جمهور أوسع.علاوة على ذلك، فإنها تعتمد بشدة على مجموعات البيانات المسمى، وهي مكلفة لجعلها.في هذا العمل، نستكشف المعنويات وتحديد المنتج على النص المتعلق بالتبغ من منصات وسائط التواصل الاجتماعي.نطلق سراح مجموعات البيانات المرسلة - Twitter و Relismoke-Reddit، إلى جانب مخطط شرح شامل لتحديد شعور منتجات التبغ.ثم نقوم بإجراء تجارب تصنيف النص باستخدام النماذج الحديثة، بما في ذلك بيرت روبرتا، والتقطير.تظهر تجاربنا نتائج F1 تصل إلى 0.72 لتحديد المعنويات في DataSet Twitter، 0.46 لتحديد المعنويات، و 0.57 للحصول على تحديد المنتج باستخدام تعلم شبه إشرافه Reddit.
Contemporary tobacco-related studies are mostly concerned with a single social media platform while missing out on a broader audience. Moreover, they are heavily reliant on labeled datasets, which are expensive to make. In this work, we explore sentiment and product identification on tobacco-related text from two social media platforms. We release SentiSmoke-Twitter and SentiSmoke-Reddit datasets, along with a comprehensive annotation schema for identifying tobacco products' sentiment. We then perform benchmarking text classification experiments using state-of-the-art models, including BERT, RoBERTa, and DistilBERT. Our experiments show F1 scores as high as 0.72 for sentiment identification in the Twitter dataset, 0.46 for sentiment identification, and 0.57 for product identification using semi-supervised learning for Reddit.
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