On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis

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.