TOM: Twitter opinion mining framework using hybrid classification scheme
By Farhan Hassan Khan, Saba Bashir, Usman Qamar. Decision Support Systems, 2014
333 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/681a0d64cdeb8af7d0138f7ed42c2589fb5f0d02
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
- Kumar Ravi
- Vadlamani Ravi
- Juan Domingo Velásquez
- Fabio A. Crestani
- Jorge A. Balazs
- Fatemeh Hemmatian
- Mohammad Karim Sohrabi
- Anastasia Giahanou
In Information Systems
- Kumar Ravi
- Vadlamani Ravi
- Juan Domingo Velásquez
- Fabio A. Crestani
- Jorge A. Balazs
- Fatemeh Hemmatian
- Mohammad Karim Sohrabi
- Anastasia Giahanou
Citations in the corpus
- Like It or Not (2016, methodology): “The framework presented by Khan et al. [2014] was built by a three-step process, the last step of which was based on a hybrid TSA method.”
- Opinion Mining and Information Fusion: A survey (2016, background): “Opinion Mining Process: Previous steps The usual Opinion Mining process or pipeline usually consists of a series of defined steps [20] [21] [22].”
- A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “What kind of algorithm should we use in this method? Graph Mincut (Blum and Chawla 2001), label propagation (LP) (Khan et al. 2014), spectral graph transducer (Joachims 2003), manifold regularization (Belkin et al.”
- A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “S# Tasks and applications #Articles References 1 Subjectivity Classification 6 [44], [75], [110], [163], [167], [174] 2 Polarity determination 43 [12], [26], [29], [32], [33], [35], [40], [45], [48], [50], [54], [57], [66], [85], [95], [96], [108], [109], [112], [114], [123], [126], [154], [156], [1”