Incremental Approach for Automatic Generation of Domain-Specific Sentiment Lexicon

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Muhammad, SH (Autor) Outra; Pavel Brazdil (Autor) FEP; Jorge, AM (Autor) FCUP

Ata de Conferência Internacional

Título: Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part II

Páginas: 619-623

42nd European Conference on IR Research, ECIR 2020

14 April 2020 through 17 April 2020

Indexação

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-00R-YSP

DOI: 10.1007/978-3-030-45442-5_81

Abstract (EN): Sentiment lexicon plays a vital role in lexicon-based sentiment analysis. The lexicon-based method is often preferred because it leads to more explainable answers in comparison with many machine learning-based methods. But, semantic orientation of a word depends on its domain. Hence, a general-purpose sentiment lexicon may gives sub-optimal performance compare with a domain-specific lexicon. However, it is challenging to manually generate a domain-specific sentiment lexicon for each domain. Still, it is impractical to generate complete sentiment lexicon for a domain from a single corpus. To this end, we propose an approach to automatically generate a domain-specific sentiment lexicon using a vector model enriched by weights. Importantly, we propose an incremental approach for updating an existing lexicon to either the same domain or different domain (domain-adaptation). Finally, we discuss how to incorporate sentiment lexicons information in neural models (word embedding) for better performance. © Springer Nature Switzerland AG 2020.

Idioma: Inglês

Tipo (Avaliação Docente): Científica