Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Guimaraes, N (Autor) Outra; Torgo, L (Autor) FCUP; Figueira, A (Autor) FCUP

Ata de Conferência Internacional

Título: SOCIAL NETWORK BASED BIG DATA ANALYSIS AND APPLICATIONS

Páginas: 1-19

8th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

San Francisco, CA, AUG 18-21, 2016

Indexação

ISI Web of Knowledge - 10 Citações

Outras Informações

ID Authenticus: P-00N-YK1

DOI: 10.1007/978-3-319-78196-9_1

Abstract (EN): Sentiment lexicons are an essential component on most state-of-the-art sentiment analysis methods. However, the terms included are usually restricted to verbs and adjectives because they (1) usually have similar meanings among different domains and (2) are the main indicators of subjectivity in the text. This can lead to a problem in the classification of short informal texts since sometimes the absence of these types of parts of speech does not mean an absence of sentiment. Therefore, our hypothesis states that knowledge of terms regarding certain events and respective sentiment (public opinion) can improve the task of sentiment analysis. Consequently, to complement traditional sentiment dictionaries, we present a system for lexicon expansion that extracts the most relevant terms from news and assesses their positive or negative score through Twitter. Preliminary results on a labelled dataset show that our complementary lexicons increase the performance of three state-of-the-art sentiment systems, therefore proving the effectiveness of our approach.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 19