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A Deep Learning–based Approach for Emotions Classification in Big Corpus of Imbalanced Tweets

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Published:15 March 2021Publication History
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Abstract

Emotions detection in natural languages is very effective in analyzing the user's mood about a concerned product, news, topic, and so on. 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.

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 3
      May 2021
      240 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3457152
      Issue’s Table of Contents

      Copyright © 2021 Association for Computing Machinery.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 15 March 2021
      • Accepted: 1 July 2020
      • Revised: 1 May 2020
      • Received: 1 May 2020
      Published in tallip Volume 20, Issue 3

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