  With the growth of social medias, such as Twitter, plenty of user-generated
data emerge daily. The short texts published on Twitter -- the tweets -- have
earned significant attention as a rich source of information to guide many
decision-making processes. However, their inherent characteristics, such as the
informal, and noisy linguistic style, remain challenging to many natural
language processing (NLP) tasks, including sentiment analysis. Sentiment
classification is tackled mainly by machine learning-based classifiers. The
literature has adopted word representations from distinct natures to transform
tweets to vector-based inputs to feed sentiment classifiers. The
representations come from simple count-based methods, such as bag-of-words, to
more sophisticated ones, such as BERTweet, built upon the trendy BERT
architecture. Nevertheless, most studies mainly focus on evaluating those
models using only a small number of datasets. Despite the progress made in
recent years in language modelling, there is still a gap regarding a robust
evaluation of induced embeddings applied to sentiment analysis on tweets.
Furthermore, while fine-tuning the model from downstream tasks is prominent
nowadays, less attention has been given to adjustments based on the specific
linguistic style of the data. In this context, this study fulfils an assessment
of existing language models in distinguishing the sentiment expressed in tweets
by using a rich collection of 22 datasets from distinct domains and five
classification algorithms. The evaluation includes static and contextualized
representations. Contexts are assembled from Transformer-based autoencoder
models that are also fine-tuned based on the masked language model task, using
a plethora of strategies.
