Classification of sentiment reviews using n-gram machine learning approach
By Abinash Tripathy, Ankit Agrawal, Santanu Kumar Rath. Expert systems with applications, 2016
544 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/4c8e6027cadcfb471de8b2954dd5ab555bae3c4b
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
- Dogu Araci
- Hossein Hassani
- Maedeh Taj Mazinani
- Stephane Unger
- Christina Beneki
- Fatemeh Hemmatian
- Mohammad Karim Sohrabi
- Mohammad Reza Yeganegi
In Information Systems
- Dogu Araci
- Hossein Hassani
- Maedeh Taj Mazinani
- Stephane Unger
- Christina Beneki
- Fatemeh Hemmatian
- Mohammad Karim Sohrabi
- Mohammad Reza Yeganegi
Citations in the corpus
- FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019, methodology): “We can divide the recent efforts into two groups: 1) Machine learning methods with features extracted from text with "word counting" [1, 19, 28, 30], 2) Deep learning methods, where text is represented by a sequence of embeddings [2, 25, 32].”
- A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “Their testing results indicate that the neural network has superiority or at least comparable resultswith SVM. InDuncan andZhang (2015), authors have examined a feed-forward neural network for sentiment analysis of tweets.”
- Text Mining in Big Data Analytics (2020, methodology): “[41] applied classification methods to classify the Internet Movie Database (IMDB) movie reviews, though they did not build a network of opinions on the movies.”