Sentiment classification: The contribution of ensemble learning

By G. Wang, Jianshan Sun, Jian Ma, Kaiquan Xu, Jibao Gu. Decision Support Systems, 2014

403 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b87d5f9b8013386f4ff5ad1a130efe6e924dca5c

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

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Citations in the corpus

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach (2020, background): “because their high accuracy and effectiveness are well-known for sentiment classification in natural language processing [42].”
Explainable Sentiment Analysis with Applications in Medicine (2018, methodology): “Ensemble methods techniques such as Bagging and Boosting were also used in order to enhance base learners performance, both for polarity and emotion classification tasks [26], [27].”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, result): “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”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “Polarity is traditionally either positive or negative (Wang et al. 2014).”
Ensemble deep learning: A review (2021, background): “2014 Recurrent Deep-Stacking Networks for sequence classification [92] Stacking Sequence classification 2014 Sentiment classification The contribution of ensemble learning [210] Bagging, Boosting, Random Subspace Sentiment classification”
News-based trading strategies (2016, background): “This demonstrates how the narrative content of disclosures [5, 6, 7, 8, 9, 10] can be harnessed to provide decision support for investors.”
Twitter Sentiment Analysis (2015, methodology): “Wang et al. evaluated the performance of ensemble methods (Bagging, Boosting, Random Subspace) and empirically proved that ensemble models can produce better results than the base learners (Wang et al., 2014).”
An Ensemble Intrusion Detection Technique Based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things (2019, methodology): “In this paper, the AdaBoost technique [25] is used, which is the most widely applied to ensemble learning mechanisms for distributing input data on the machine learning techniques used.”