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Register a new userA Corpus for Dimensional Sentiment Classification on YouTube Streaming Service
كوربوس لتصنيف المعنويات الأبعاد على خدمة تدفق يوتيوب
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توفر منصة خدمة البث مثل YouTube وظيفة مناقشة للجماهير في جميع أنحاء العالم لمشاركة التعليقات. YouTubers الذين يقومون بتحميل مقاطع الفيديو على منصة YouTube ترغب في تتبع أداء مقاطع الفيديو التي تم تحميلها. ومع ذلك، فإن مهام التحليل الحالية من YouTube توفر فقط بعض مؤشرات الأداء مثل متوسط مدة العرض، سجل التصفح، والتباين في التركيبة السكانية للجمهور، وما إلى ذلك، ونقص تحليل المعنويات بشأن تعليقات الجمهور. لذلك، تقترح الورقة مؤشرات المعنويات متعددة الأبعاد مثل تفضيلات YouTuber وتفضيلات الفيديو ومستوى الإثارة لالتقاط مشاعر شاملة بشأن تعليقات الجمهور لمقاطع الفيديو ويوتيوغتر. لتقييم أداء الطبقات المختلفة، نقوم بتجربة معصوص قائما على التعلم والتعلم ومقرها في التعلم، ويكتشف تلقائيا ثلاثة مؤشرات معنويات لتصريحات الجمهور. تشير النتائج التجريبية إلى أن المصنف المستند إلى BERT يعد نموذج تصنيف أفضل من الطبقات الأخرى وفقا لنتيجة F1، ومؤشر المعنويات على مستوى الإثارة هو تحسن تماما. لذلك، يمكن حل مهام الكشف عن المعنويات المتعددة على منصة خدمة تدفق الفيديو من خلال مؤشرات المعنويات متعددة الأبعاد المقترحة مصحوبة مع مصنف Bert للحصول على أفضل نتيجة.
The streaming service platform such as YouTube provides a discussion function for audiences worldwide to share comments. YouTubers who upload videos to the YouTube platform want to track the performance of these uploaded videos. However, the present analysis functions of YouTube only provide a few performance indicators such as average view duration, browsing history, variance in audience's demographics, etc., and lack of sentiment analysis on the audience's comments. Therefore, the paper proposes multi-dimensional sentiment indicators such as YouTuber preference, Video preferences, and Excitement level to capture comprehensive sentiment on audience comments for videos and YouTubers. To evaluate the performance of different classifiers, we experiment with deep learning-based, machine learning-based, and BERT-based classifiers to automatically detect three sentiment indicators of an audience's comments. Experimental results indicate that the BERT-based classifier is a better classification model than other classifiers according to F1-score, and the sentiment indicator of Excitement level is quite an improvement. Therefore, the multiple sentiment detection tasks on the video streaming service platform can be solved by the proposed multi-dimensional sentiment indicators accompanied with BERT classifier to gain the best result.
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Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interp
ret why a sentiment polarity is derived for the aspect. To address this problem, recent works fine-tune pre-trained Transformer encoders for ALSC to extract an aspect-centric dependency tree that can locate the opinion words. However, the induced opinion words only provide an intuitive cue far below human-level interpretability. Besides, the pre-trained encoder tends to internalize an aspect's intrinsic sentiment, causing sentiment bias and thus affecting model performance. In this paper, we propose a span-based anti-bias aspect representation learning framework. It first eliminates the sentiment bias in the aspect embedding by adversarial learning against aspects' prior sentiment. Then, it aligns the distilled opinion candidates with the aspect by span-based dependency modeling to highlight the interpretable opinion terms. Our method achieves new state-of-the-art performance on five benchmarks, with the capability of unsupervised opinion extraction.
This paper presents the ROCLING 2021 shared task on dimensional sentiment analysis for educational texts which seeks to identify a real-value sentiment score of self-evaluation comments written by Chinese students in the both valence and arousal dime
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Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed
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Recent work on aspect-level sentiment classification has employed Graph Convolutional Networks (GCN) over dependency trees to learn interactions between aspect terms and opinion words. In some cases, the corresponding opinion words for an aspect term
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