Cargando…
Specialists, Scientists, and Sentiments: Word2Vec and Doc2Vec in Analysis of Scientific and Medical Texts
Analyze performance of unsupervised embedding algorithms in sentiment analysis of knowledge-rich data sets. We apply state-of-the-art embedding algorithms Word2Vec and Doc2Vec as the learning techniques. The algorithms build word and document embeddings in an unsupervised manner. To assess the algor...
Autores principales: | Chen, Qufei, Sokolova, Marina |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Singapore
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364743/ https://www.ncbi.nlm.nih.gov/pubmed/34414378 http://dx.doi.org/10.1007/s42979-021-00807-1 |
Ejemplares similares
-
Improving the Polarity of Text through word2vec Embedding for Primary Classical Arabic Sentiment Analysis
por: Aoumeur, Nour Elhouda, et al.
Publicado: (2023) -
SAO2Vec: Development of an algorithm for embedding the subject–action–object (SAO) structure using Doc2Vec
por: Kim, Sunhye, et al.
Publicado: (2020) -
The Spectral Underpinning of word2vec
por: Jaffe, Ariel, et al.
Publicado: (2020) -
Word2Vec inversion and traditional text classifiers for phenotyping lupus
por: Turner, Clayton A., et al.
Publicado: (2017) -
Sentiment Thesaurus, Synset and Word2Vec Based Improvement in Bigram Model for Classifying Product Reviews
por: Poomagal, S., et al.
Publicado: (2022)