Ontology-based sentiment analysis of twitter posts
By Efstratios Kontopoulos, Christos Berberidis, Theologos Dergiades, Nick Bassiliades. Expert systems with applications, 2013
395 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/46b5c87d49ac2dc9085b4f7afc3660c7b8f4abc5
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
- Vadlamani Ravi
- Saeed Reza Aghabozorgi
- Teh Ying Wah
- Enrique Herrera-Viedma
- Kumar Ravi
- Fabio A. Crestani
- Arman Khadjeh Nassirtoussi
- David Chek Ling Ngo
In Information Systems
- Enrique Herrera-Viedma
- Kumar Ravi
- Vadlamani Ravi
- Fabio A. Crestani
- Teh Ying Wah
- Arman Khadjeh Nassirtoussi
- David Chek Ling Ngo
- Saeed Reza Aghabozorgi
Citations in the corpus
- A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “[96] analyzed sentiment of tweets using ontological engineering.”
- Sentiment analysis: A review and comparative analysis of web services (2015, background): “, a collection of known and precompiled sentiment terms, phrases and even idioms, developed for traditional genres of communication, such as the Opinion Finder lexicon [95]; but, even more complex structures like ontologies [39], or dictionaries measuring the semantic orientation of words or phrases”
- Like It or Not (2016, methodology): “Formal Concept Analysis (FCA), proposed by Kontopoulos et al. [2013], is one of those techniques.”
- Text mining for market prediction: A systematic review (2014, background): “Li, Yang, and Park (2012) demonstrate high performance of k-NN for text categorization as well as Jiang, Pang, Wu, and Kuang (2012).”