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Register a new userBERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter
BERT GOAN BRRR: مشروعا تجاه خطأ أقل في تصنيف مراسلين ذوي الذات الطبي على تويتر
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تصف هذه الورقة تقديم فريقنا من أجل تعدين وسائل التواصل الاجتماعي للصحة (SMM4H) 2021 المهمة المشتركة.شاركنا في ثلاث مجموعات فرعية: تصنيف تأثير المخدرات السلبي، والتقرير الذاتي Covid-19، وأعراض Covid-19.يعتمد نظامنا على نموذج Bert المدرب مسبقا على النص الخاص بالمجال.بالإضافة إلى ذلك، نقوم بإجراء تنظيف البيانات والتكبير، بالإضافة إلى تحسين فرط التنفس وفرقة نموذجية لتعزيز أداء بيرت.حققنا الرتبة الأولى في كل من تأثيرات المخدرات الضارة ومهام التقرير الذاتي CovID-19.
This paper describes our team's submission for the Social Media Mining for Health (SMM4H) 2021 shared task. We participated in three subtasks: Classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms. Our system is based on BERT model pre-trained on the domain-specific text. In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance. We achieved the first rank in both classifying adverse drug effects and COVID-19 self-report tasks.
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We bring the data from the social networking site Twitter
pages, and then we have worked on cleaning and processing
operation to the text of for the classification process texts retrieved
contain a lot of noise and information is useful for the pr
ocess of
analyzing the views, such as advertisements and links and e-mail
addresses and the presence of many words that do not affect the
general orientation of the text, and then get all the publications in
the Twitter page and what are the comments about each tweets is
intended to know the proportion of supporters and opponents of
this publication.
We apply Naïve Bayes algorithm in classification, we had the
appropriate training, and after passing Posts and comments data
(opinions), we got good results on the ratio of supporters of the
post and the percentage of his opponents.
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