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However, it is really a challenging task to extract important features from a burst of raw social text, as emotions are subjective with limited fuzzy boundaries. These subjective features can be conveyed in various perceptions and terminologies. In this article, we proposed an IoT-based framework for emotions classification of tweets using a hybrid approach of Term Frequency Inverse Document Frequency (TFIDF) and deep learning model. First, the raw tweets are filtered using the tokenization method for capturing useful features without noisy information. Second, the TFIDF statistical technique is applied to estimate the importance of features locally as well as globally. Third, the Adaptive Synthetic (ADASYN) class balancing technique is applied to solve the imbalance class issue among different classes of emotions. Finally, a deep learning model is designed to predict the emotions with dynamic epoch curves. The proposed methodology is analyzed on two different Twitter emotions datasets. The dynamic epoch curves are shown to show the behavior of test and train data points. It is proved that this methodology outperformed the popular state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3410570","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T20:10:26Z","timestamp":1615839026000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["A Deep Learning\u2013based Approach for Emotions Classification in Big Corpus of Imbalanced Tweets"],"prefix":"10.1145","volume":"20","author":[{"given":"Nasir","family":"Jamal","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Xianqiao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fadi","family":"Al-Turjman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Dept., Research Center for AI and IoT, Near East University, Nicosia, Mersin, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farhan","family":"Ullah","sequence":"additional","affiliation":[{"name":"School of Software, Northwestern Polytechnical University, Xi'an Shaanxi, P.R. 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