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Detecting Emotions in English and Arabic Tweets

1
School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
2
CAS, Arabic Department, Qatar University, Al Hala St, P.O. Box 2713 Doha, Qatar
*
Author to whom correspondence should be addressed.
Information 2019, 10(3), 98; https://doi.org/10.3390/info10030098
Received: 21 January 2019 / Revised: 18 February 2019 / Accepted: 28 February 2019 / Published: 6 March 2019
(This article belongs to the Special Issue Artificial Intelligence—Methodology, Systems, and Applications)
Assigning sentiment labels to documents is, at first sight, a standard multi-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. View Full-Text
Keywords: sentiment mining; shallow learning; multi-emotion classification sentiment mining; shallow learning; multi-emotion classification
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Ahmad, T.; Ramsay, A.; Ahmed, H. Detecting Emotions in English and Arabic Tweets. Information 2019, 10, 98.

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