2016
DOI: 10.1007/978-981-10-1023-1_13
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An Innovative ‘Cluster-then-Predict’ Approach for Improved Sentiment Prediction

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Cited by 9 publications
(3 citation statements)
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“…Based on the important features identified, we adopt the cluster-then-predict approach to build the final AI models [22], [23]. Specifically, we first cluster the patients into subgroups, and then train a decision tree classifier for each subgroup of patients.…”
Section: The Cluster-then-predict Approachmentioning
confidence: 99%
“…Based on the important features identified, we adopt the cluster-then-predict approach to build the final AI models [22], [23]. Specifically, we first cluster the patients into subgroups, and then train a decision tree classifier for each subgroup of patients.…”
Section: The Cluster-then-predict Approachmentioning
confidence: 99%
“…The cluster-then-predict models employed in this study can be separated into three different semi-supervised and independent classification models. Unsupervised methods are used to find clusters in the dataset, and a supervised model is subsequently used to assign labels to the cluster (Soni and Mathai, 2015;Trivedi et al, 2015). As an unsupervised model, k-means clustering (Forgy, 1965;Lloyd, 1982), mixture model clustering (GMM) (Bishop, 2006), and Bayesian Gaussian mixture models (BGMM) (Bishop, 2006) were used.…”
Section: Modelsmentioning
confidence: 99%
“…It is considered to be relatively computational efficient. In (Soni and Mathai, 2015), a 'cluster-then-predict' model was proposed to improve the accuracy of predicting Twitter sentiment through a composition of both supervised and unsupervised learning. After building the dataset, k-Means was performed such that tweets with similar words are clustered together.…”
Section: Hard Clusteringmentioning
confidence: 99%