Tweet sentiment analysis with classifier ensembles
By Nádia Silva, Eduardo R. Hruschka, Estevam R. Hruschka. Decision Support Systems, 2014
452 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/7f9a295832dc38807cf09a47eb95d5d5a312e8b2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Fabio A. Crestani
- Taghi M. Khoshgoftaar
- Sahar Sohangir
- Haijun Lv
- Ning Chen
- Yongquan Yang
- Anna Pomeranets
- Dingding Wang
In Information Systems
- Fabio A. Crestani
- Taghi M. Khoshgoftaar
- Sahar Sohangir
- Haijun Lv
- Ning Chen
- Yongquan Yang
- Anna Pomeranets
- Dingding Wang
Citations in the corpus
- Like It or Not (2016, result): “On the contrary, da Silva et al. [2014] employed more classifiers compared to Lin and Kolcz [2012].”
- A Survey on ensemble learning under the era of deep learning (2021, methodology): “The obtained base deep learners were integrated to form the final ensemble deep learner through weighting by majority voting or meta-learning by random forests (Da Silva et al. 2014; Zhang and He 2015).”
- Big Data: Deep Learning for financial sentiment analysis (2018, methodology): “Supervised classification methods, such as Support Vector Machines [48], Naïve Bayes [49] or ensembles [50, 51] have been deployed to perform sentiment analysis in multiple research projects.”