مع النمو السريع في التكنولوجيا، شهد نشاط وسائل التواصل الاجتماعي طفرة في جميع الفئات العمرية.من المستحيل الإنساني التحقق من جميع التغريدات والتعليقات والحالة يدويا ما إذا كانت تتبع إرشادات المجتمع المناسبة.يتم نشر الكثير من السمية بانتظام على منصات وسائل التواصل الاجتماعي هذه.يهدف هذا البحث إلى إيجاد كلمات سامة في جملة بحيث يتم بناء مجتمع اجتماعي صحي في جميع أنحاء العالم ويتلقى المستخدمون محتوى مراقم مع تحذيرات وحقائق محددة.لحل هذه المشكلة الصعبة، جمع المؤلفون مفاهيم القائمة المرتبطة بمعالجة ما قبل المعالجة ثم استخدمت فكرة المدينات المكدسة مثل Adffeddings Bertdings و Argeddings Flair و Word2VEC على إطار Flairnlp للحصول على النتائج المرجوة.تم استخدام F1 متري لتقييم النموذج.تمكن المؤلفون من إنتاج درجة 0.74 F1 في مجموعة الاختبار الخاصة بهم.
With the rapid growth in technology, social media activity has seen a boom across all age groups. It is humanly impossible to check all the tweets, comments and status manually whether they follow proper community guidelines. A lot of toxicity is regularly posted on these social media platforms. This research aims to find toxic words in a sentence so that a healthy social community is built across the globe and the users receive censored content with specific warnings and facts. To solve this challenging problem, authors have combined concepts of Linked List for pre-processing and then used the idea of stacked embeddings like BERT Embeddings, Flair Embeddings and Word2Vec on the flairNLP framework to get the desired results. F1 metric was used to evaluate the model. The authors were able to produce a 0.74 F1 score on their test set.
References used
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