Bayesian network based extreme learning machine for subjectivity detection
By Iti Chaturvedi, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino, Erik Cambria. 2017
153 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
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
- Erik Cambria
- Soujanya Poria
- Devamanyu Hazarika
- Tom Young
- Frank Xing
- Navonil Majumder
- Haiyun Peng
- Roy E. Welsch
In Electrical and Electronic Engineering
Citations in the corpus
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, methodology): “More specific use cases include applications such as multilabel text categorization [76], multimodal sentiment analysis [77]–[79], and subjectivity detection [80].”
- Natural language based financial forecasting: a survey (2017, background): “Sentiment analysis [16] is a ‘suitcase research problem’ [19] that requires tackling many NLP sub-tasks, including aspect extraction [92], subjectivity detection [27], named entity recognition [73], and sarcasm detection [93], but also complementary tasks such as personality recognition [74], user p”
- Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM (2018, background): “Sentiment analysis is a branch of affective computing research (Poria et al. 2017) that aims to classify text into either positive or negative, but sometimes also neutral (Chaturvedi et al. 2017).”
- Sentiment Analysis Is a Big Suitcase (2017, unknown): “12”