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.

中文翻译：

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一种基于深度学习的不平衡推文大语料库中情绪分类的方法

自然语言中的情绪检测在分析用户对相关产品、新闻、话题等的情绪方面非常有效。然而，从大量原始社交文本中提取重要特征确实是一项具有挑战性的任务，因为情绪是主观的，具有有限的模糊边界。这些主观特征可以用各种看法和术语来表达。在本文中，我们提出了一种基于物联网的推文情绪分类框架，使用词频逆文档频率 (TFIDF) 和深度学习模型的混合方法。首先，使用标记化方法对原始推文进行过滤，以捕获有用的特征而没有噪声信息。其次，应用 TFIDF 统计技术来估计局部和全局特征的重要性。第三，自适应综合（ADASYN）类平衡技术用于解决不同类别情绪之间的不平衡类问题。最后，设计了一个深度学习模型来预测具有动态时期曲线的情绪。所提出的方法在两个不同的 Twitter 情绪数据集上进行了分析。显示动态历元曲线以显示测试和训练数据点的行为。事实证明，这种方法优于流行的最先进的方法。