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المؤلفAhmad, Tariq
المؤلفRamsay, Allan
المؤلفAhmed, Hanady
تاريخ الإتاحة2020-08-18T08:34:43Z
تاريخ النشر2019
اسم المنشورInformation (Switzerland)
المصدرScopus
الرقم المعياري الدولي للكتاب20782489
معرّف المصادر الموحدhttp://dx.doi.org/10.3390/info10030098
معرّف المصادر الموحدhttp://hdl.handle.net/10576/15621
الملخصAssigning sentiment labels to documents is, at first sight, a standardmulti-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms. - 2019 by the authors.
راعي المشروعFunding: This publication was made possible by the NPRP award (NPRP 7-1334-6-039 PR3) from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the author(s).
اللغةen
الناشرMDPI AG
الموضوعMulti-emotion classification
Sentiment mining
Shallow learning
العنوانDetecting emotions in English and Arabic tweets
النوعArticle
رقم العدد3
رقم المجلد10
dc.accessType Abstract Only


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