Identification of emotion hidden in limited text is an active research problem. This work presents a framework for the same using email text. The present work is based on machine learning methods and utilizes three classifiers and three feature selection methods. The novelty of the proposed framework is the utilization of in-text features to identify emotion contained in short texts and development of a dataset for this purpose. Six emotions, namely, neutral, happy, sad, angry, positively surprised, and negatively surprised are utilized here based on baseline theories on human emotion. Experiments are performed on three datasets including a benchmark and one local dataset. These experiments are performed by extracting 14 in-text features from the data. The proposed framework is evaluated using four standard evaluation metrics. Based on the feature selection results, experiments are performed on the datasets under consideration by vertically partitioning them into all features, top features, and bottom features. Qualitative and quantitative comparison of the proposed work is also made with two state-of-the-art methods. The obtained results suggest better performance of the current work with an average accuracy of 83%. The proposed framework can be utilized in an assortment of domains to identify human emotion by providing limited text as an input.

识别隐藏在有限文本中的情感是一个积极的研究问题。这项工作提出了使用电子邮件文本的框架。本工作基于机器学习方法，并利用三个分类器和三个特征选择方法。所提出的框架的新颖性是利用文本特征来识别短文本中包含的情感，并为此目的开发数据集。六种情绪，即中立，快乐，悲伤，愤怒，积极惊讶和消极惊讶在此基于关于人类情感的基线理论进行利用。在包括基准和一个本地数据集的三个数据集上进行实验。这些实验是通过从数据中提取14个文本特征来执行的。使用四个标准评估指标对提出的框架进行评估。根据特征选择结果，通过将数据集垂直划分为所有特征，顶部特征和底部特征，对所考虑的数据集进行实验。还使用两种最先进的方法对拟议工作进行了定性和定量比较。获得的结果表明，当前工作的性能更好，平均精度为83％。通过提供有限的文本作为输入，可以在各种领域中利用提出的框架来识别人类情感。