Abstract
The Natural Language Processing field has made great strides recently. As a result, many challenging tasks are being given better solutions. One of these tasks is Sentiment Analysis, which is the subject of this work. We propose a novel methodology to classify the sentiment of tweets, based on BERT and focusing on emoji. Our method also employs data augmentation to improve its generalization ability. Experiments on two Brazilian Portuguese datasets – TweetSentBR and 2000-tweets-BR – show that our methodology produces better results than BERT and outperforms the previously published results for TweetSentBR, with accuracy of 0.7726 (6.3 percentage points (p.p.) of improvement) and F\(_{1}\) score of 0.7514 (9.5 p.p. of improvement), as well as for 2000-tweets-BR, with accuracy of 0.8247 (14.5 p.p. of improvement) and F\(_{1}\) score of 0.8035 (23.4 p.p. of improvement).
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Jessica is beautiful and has a sense of humor.
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When was that? Didn’t she win with a gnocchi recipe?
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That’s terrible... it’s like a horror freak show.
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Emoticons considered: \(\texttt {S2\;\;{<}3\;\,{;}D\;\,{:}D\;\;{;}-)\;\;{:}-)\;\,{;})\;\,{=})\;\;{:})\;\;{;}-(\;\;{:}-(\,\,{;}(\;\,{=}(\;\,{:}(}\).
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Acknowledgements
The authors would like to thank FAPESP (grants #2015/11937-9, #2017/12646-3, #2017/16246-0, #2017/12646-3 and #2019/20875-8), CNPq (grants #304380/2018-0 and #309330/2018-1) and CAPES for their financial support.
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de Barros, T.M., Pedrini, H., Dias, Z. (2021). Data-Augmented Emoji Approach to Sentiment Classification of Tweets. In: Tavares, J.M.R.S., Papa, J.P., González Hidalgo, M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2021. Lecture Notes in Computer Science(), vol 12702. Springer, Cham. https://doi.org/10.1007/978-3-030-93420-0_7
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