Combining lexicon-based and learning-based methods for twitter sentiment analysis

By Lei Zhang, Riddhiman Ghosh, Mohamed Dekhil, Meichun Hsu, B. Liu. 2011

505 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ab9a7687ab7c90707f863e54afe12fd99f2deb11

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Anastasia Giahanou
2. Fabio A. Crestani
3. J. Skinner
4. David Zimbra
5. Manoochehr Ghiassi
6. Kumar Ravi
7. Vadlamani Ravi
8. B. Shravan Kumar

Citations in the corpus

• Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, methodology): “…although support vector machines (Agarwal et al., 2011; Barbosa & Feng, 2010; Bermingham & Smeaton, 2010; Go et al., 2009; Jiang et al., 2011; Zhang et al., 2011) and naïve Bayes models are most often applied (Go et al., 2009; Bermingham & Smeaton, 2010; Pak & Paroubek, 2010; Saif, He, &…”
• Like It or Not (2016, methodology): “Coreference resolution that occurs when two or more expressions refer to the same person or thing is an additional syntactic feature that has been examined for TSA [Zhang et al. 2011].”