Ensemble of feature sets and classification algorithms for sentiment classification

By Rui Xia, Chengqing Zong, Shoushan Li. Information Sciences, 2011

640 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/848848a8a03a1100f726179f024b36d43cc38cd7

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In Artificial Intelligence

Citations in the corpus

A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “…(2011) 64 Li and Liu (2012) 77 Pang and Lee (2004) Penalver-Martinez et al. (2014) 89.6 Fernández-Gavilanes et al. (2016) 69.95 Fersini et al. (2016) 81.7 Saleh et al. (2011) 85.35 Boiy and Moens (2009) 87.40 Blitzer et al. (2007) Xia et al. (2016) 80 Xia et al. (2011) 85.58 Poria et al. (2014) 87”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, background): “[54] ensembled feature sets and machine learning for sentiment classification.”
Improving Hate Speech Detection with Deep Learning Ensembles (2018, methodology): “Ensemble methods for text classiﬁcation, such as stacking and bagging, are commonly used approaches (Ag-garwal and Zhai, 2012; Xia et al., 2011).”
Sentiment analysis in twitter using machine learning techniques (2013, background): “Since social network generates huge amount of data every second, sometimes larger than the size of available lexical database, sentiment analysis became tedious and erroneous.”
Distantly Supervised Lifelong Learning for Large-Scale Social Media Sentiment Analysis (2017, background): “The domain adaptive property of Lifelong Stacking can be interpreted as follows [37], [38].”
Sentiment Analysis of Twitter Data : A Survey of Techniques (2016, methodology): “Following are some of the challenges[13] faced in Sentiment Analysis of Twitter.”
Using sensitivity analysis and visualization techniques to open black box data mining models (2013, background): “Several learning models/algorithms are available for these tasks, each one with its own advantages.”