Twitter has become a medium through which a substantial percentage of the global population communicates their feelings and reactions to current events. Emotion mining from text aims to capture these emotions by using a series of algorithms to evaluate the contents of each tweet. In this study, tweets that expressed at least one of seven basic emotions were collected. The resulting dataset was a corpus of 42,000 tweets with a balanced presence of each emotion. From this corpus a lexicon of roughly 40,000 words, each associated with a weighted vector corresponding to one of the emotions, was created. Next, different methods of identifying emotion in these ‘cleaned’ tweets were performed and evaluated. These methods included both lexically-based classification and supervised machine learning-based classification. Finally, an ensemble method involving several multi-class classifiers trained on unigram features of the lexicon was evaluated. This evaluation revealed that the ensemble...

中文翻译：

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Twitter上的文本情感挖掘

Twitter已成为一种媒介，通过它，全球大部分人口都可以交流他们对时事的看法和反应。从文本进行情感挖掘旨在通过使用一系列算法来评估每个推文的内容来捕获这些情感。在这项研究中，收集了表达至少七个基本情绪之一的推文。最终的数据集是一个42,000条推文的语料库，每种情感都有均衡的存在。从该语料库中创建了大约40,000个单词的词典，每个词典都与对应于其中一种情绪的加权向量相关联。接下来，执行并评估了在这些“干净的”推文中识别情感的不同方法。这些方法包括基于词汇的分类和基于监督的机器学习的分类。最后，评估了一种综合方法，该方法涉及对词典的字母组合特征进行训练的多个多类分类器。评估显示该合奏...