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Register a new userExploring Implicit Sentiment Evoked by Fine-grained News Events
استكشاف المشاعر الضمنية أثارت أحداث الأخبار الدقيقة
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نحن نبحث في جدوى تحديد المشاعر التي أثارها أحداث الأخبار المحبوبة.يستند سؤالنا البحثي إلى الفرضية التي يمكن أن تكون أساليب الكشف عن المشاعر الضمنية في الأخبار سائقا رئيسيا لتنوع المحتوى، وهي طريقة واحدة للتخفيف من الآثار الضارة لفقاعات المرشح التي قد تنتج إليها التصفية التعاونية.تستند تجاربنا إلى 1735 مقال إخباري من الصحف الفلمنكية الرئيسية التي تم تفاحها يدويا، مع اتفاق مرتفع، للمعنات الضمنية.في حين أن الموارد المعجمية تثبت عدم كفاية تحليل المعنويات في هذا النوع من البيانات، فإن نتائجنا توضح أن نماذج التعلم الآلي المستندة إلى SVM و Bert قادرة على استنتاج المشاعر الضمنية تلقائيا من خلال أحداث الأخبار.
We investigate the feasibility of defining sentiment evoked by fine-grained news events. Our research question is based on the premise that methods for detecting implicit sentiment in news can be a key driver of content diversity, which is one way to mitigate the detrimental effects of filter bubbles that recommenders based on collaborative filtering may produce. Our experiments are based on 1,735 news articles from major Flemish newspapers that were manually annotated, with high agreement, for implicit sentiment. While lexical resources prove insufficient for sentiment analysis in this data genre, our results demonstrate that machine learning models based on SVM and BERT are able to automatically infer the implicit sentiment evoked by news events.
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