Sentiment analysis of short informal texts, such as tweets, remains a challenging task due to their particular characteristics. Much effort has been made in the literature of Twitter sentiment analysis to achieve an effective and efficient representation of tweets. In this context, distinct types of features have been proposed and employed, from the simple n-gram representation to meta-features to word embeddings. Hence, in this work, using a relevant set of twenty-two datasets of tweets, we present a thorough evaluation of features by means of different supervised learning algorithms. We evaluate not only a rich set of meta-features examined in state-of-the-art studies, but also a significant collection of pre-trained word embedding models. Also, we evaluate and analyze the effect of combining those distinct types of features in order to detect which combination may provide core information in the polarity detection task in Twitter sentiment analysis. For this purpose, we exploit different strategies for combination, such as feature concatenation and ensemble learning techniques, and show that the sentiment detection of tweets benefits from combining different types of features proposed in the literature.

中文翻译：

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关于 Twitter 情感分析中 state-of-the-art 特征的评估和组合

由于其特殊的特性，对简短的非正式文本（如推文）的情感分析仍然是一项具有挑战性的任务。在 Twitter 情感分析的文献中已经做出了很多努力，以实现对推文的有效和高效表示。在这种情况下，已经提出并采用了不同类型的特征，从简单的 n-gram 表示到元特征再到词嵌入。因此，在这项工作中，我们使用一组相关的 22 个推文数据集，通过不同的监督学习算法对特征进行了全面评估。我们不仅评估了在最先进的研究中检查过的一组丰富的元特征，而且还评估了大量预训练的词嵌入模型。还，我们评估和分析组合这些不同类型的特征的效果，以检测哪种组合可以在 Twitter 情感分析的极性检测任务中提供核心信息。为此，我们利用不同的组合策略，例如特征连接和集成学习技术，并表明推文的情感检测受益于组合文献中提出的不同类型的特征。