Abstract
Unsupervised learning is an emerging approach in sentiment analysis. In this paper, we apply unsupervised word and document embedding algorithms, Word2Vec and Doc2Vec, to medical and scientific text. We use SentiWordNet as the benchmark measures. Our empirical study is done on the Obesity NLP Challenge data set and four Science subgroups from Reuters 20 Newsgroups. Our results show that Word2Vec demonstrates a reliable performance in sentiment analysis of the text, whereas Doc2Vec requires more detailed studies.
Cite
CITATION STYLE
Chen, Q., & Sokolova, M. (2019). Unsupervised Sentiment Analysis of Objective Texts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11489 LNAI, pp. 460–465). Springer Verlag. https://doi.org/10.1007/978-3-030-18305-9_45
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
