Abstract
During the past years, we have seen a steady increase in the number of social networks worldwide. Among them, Twitter has consolidated its position as one of the most influential social platforms, with Brazilian Portuguese speakers holding the fifth position in the number of users. Due to the informal linguistic style of tweets, the discovery of information in such an environment poses a challenge to Natural Language Processing (NLP) tasks such as sentiment analysis. In this work, we state sentiment analysis as a binary (positive and negative) and multiclass (positive, negative, and neutral) classification task at the Portuguese-written tweet level. Following a feature extraction approach, embeddings are initially gathered for a tweet and then given as input to learning a classifier. This study was designed to evaluate the effectiveness of different word representations, from the original pre-trained language model to continued pre-training strategies, to improve the predictive performance of sentiment classification, using three different classifier algorithms and eight Portuguese tweets datasets. Because of the lack of a language model specific to Brazilian Portuguese tweets, we have expanded our evaluation to consider six different embeddings: fastText, GloVe, Word2Vec, BERT-multilingual (mBERT), BERTweet, and BERTimbau. The experiments showed that embeddings trained from scratch solely using the target Portuguese language, BERTimbau, outperform the static representations, fastText, GloVe, and Word2Vec, and the Transformer-based models BERT multilingual and BERTweet. In addition, we show that extracting the contextualized embedding without any adjustment to the pre-trained language model is the best approach for most datasets.
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Acknowledgements
This research was partially financed by CNPq (National Council for Scientific and Technological Development) under Grants 311275/2020-6 and 315750/2021-9 and FAPERJ—Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Process SEI-260003/000614/2023, E-26/202.914/2019 and E-26/201.139/2022.
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Appendix: Evaluation of static embeddings
Appendix: Evaluation of static embeddings
An initial evaluation using three different static embeddings, fastText, Word2Vec, and GloVe, was performed to identify the best static embedding for our scenario. Tables 38, 39, and 40 present Acc and F1-score (F1) for all three static embeddings and classifiers LR, SVM, and XGB, respectively. The superiority of fastText is clear for all datasets when combined with classifiers LR and SVM, and almost all datasets when used with the XGB classifier. The same can be seen with the multiclass set of datasets, as shown in Tables 41, 42, and 43. The superiority of fastText can be explained by its ability to deal with subwords, an important feature when working with tweets since those usually follow an informal style with heavy use of abbreviations and misspelling words.
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Vianna, D., Carneiro, F., Carvalho, J. et al. Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation models. Lang Resources & Evaluation 58, 223–272 (2024). https://doi.org/10.1007/s10579-023-09661-4
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DOI: https://doi.org/10.1007/s10579-023-09661-4