Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/90745
Título: | Enhancing sentiment analysis using syntactic patterns |
Autor(es): | Milhazes, Ricardo Belo, Orlando |
Palavras-chave: | Hearst Patterns Machine Learning Natural Language processing Sentiment Analysis Syntactic Patterns Text Mining |
Data: | 2023 |
Editora: | Springer, Cham |
Revista: | Lecture Notes in Networks and Systems |
Citação: | Milhazes, R., Belo, O. (2023). Enhancing Sentiment Analysis Using Syntactic Patterns. In: Rocha, Á., Ferrás, C., Ibarra, W. (eds) Information Technology and Systems. ICITS 2023. Lecture Notes in Networks and Systems, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-031-33258-6_32 |
Resumo(s): | Using specialized analysis tools, combining natural language processing techniques with machine-learning-based sentiment analysis, it is possible to establish positive and negative sentiments expressed in opinion texts. Thus, organizations have the possibility to act in an adequate way, having the opportunity to improve their relationship with their customers and improve their loyalty, according to the type of sentiment identified. In this paper we present and describe a sentiment analysis system especially developed to identify sentiments, of different polarities, expressed in opinion texts of students of an eLearning application. We have slightly rewritten the usual way of approaching sentiment analysis problems by using Hearst patterns, for improving classification models efficiency, valuing the sentiments expressed in a wider scale of classification values. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/90745 |
ISBN: | 978-3-031-33257-9 |
e-ISBN: | 978-3-031-33258-6 |
DOI: | 10.1007/978-3-031-33258-6_32 |
ISSN: | 2367-3370 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-031-33258-6_32 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
2023-ICITS-Milhazes&Belo-CRP.pdf Acesso restrito! | 681,38 kB | Adobe PDF | Ver/Abrir |