Sentiment analysis in tweets : an assessment study from classical to modern word representation models
© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author sel...
| Veröffentlicht in: | Data mining and knowledge discovery. - 2003. - 37(2023), 1 vom: 10., Seite 318-380 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2023
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| Zugriff auf das übergeordnete Werk: | Data mining and knowledge discovery |
| Schlagworte: | Journal Article Language models Natural language processing Sentiment analysis Text representations Twitter |
| Zusammenfassung: | © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. With the exponential growth of social media networks, 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 different types of word representation models 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 modeling, 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 fulfills an assessment of existing neural 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 adapted based on the masked language model task, using a plethora of strategies |
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| Beschreibung: | Date Revised 11.01.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1384-5810 |
| DOI: | 10.1007/s10618-022-00853-0 |