Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation models.
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 inform...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 1; pp. 223 - 273 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Mar2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=176079988&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 176079988 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Mar2024 vid: 58 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 176079988 10.1007/s10579-023-09661-4 ppf: 223 ppct: 50 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.2MB tig: atl: Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation models. aug: au: Vianna, Daniela Carneiro, Fernando Carvalho, Jonnathan Plastino, Alexandre Paes, Aline affil: https://ror.org/02263ky35 Institute of Computing, Universidade Federal do Amazonas (UFAM), Manaus, AM, Brazil Jusbrasil, Salvador, Brazil https://ror.org/02rjhbb08 Institute of Computing, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil https://ror.org/0009eqg37 Instituto Federal Fluminense (IFF), Itaperuna, RJ, Brazil su: X Corp. Sentiment analysis Natural language processing Language models Portuguese language Social media sug: subj: X Corp. Sentiment analysis Natural language processing Language models Portuguese language Social media keyword: Brazilian Portuguese tweets Word representation ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
|---|