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Register a new userWRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations
التمهيد: مجموعة بيانات جديدة لتقدير الشدة العاطفية مع التعليقات الإعلانية ذاتية والهدف
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سنلقاشر 17000 وظيفة SNS مع كل من الكثافة العاطفية الشخصية للكاتب وهدف القارئ واحد لبناء مجموعة بيانات تحليل العاطفة اليابانية.في هذه الدراسة، نستكشف الفرق بين الكثافة العاطفية للكاتب والقراء مع هذه البيانات.وجدنا أن القارئ لا يمكن أن يكتشف تماما عواطف الكاتب، وخاصة الغضب والثقة.بالإضافة إلى ذلك، تظهر النتائج التجريبية في تقدير الشدة العاطفية أنه من الصعب تقدير الملصقات الشخصية للكاتب من القراء.الفجوة الكبيرة بين المشاعر الذاتية والموضوعية تعني تعقيد التعيين من منشور إلى شدة العاطفة الذاتية، والتي تؤدي أيضا إلى أداء أقل مع نماذج تعلم الآلة.
We annotate 17,000 SNS posts with both the writer's subjective emotional intensity and the reader's objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer's subjective labels than the readers'. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.
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