Design of adaptive ensemble classifier for online sentiment analysis and opinion mining


DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.

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Publication: Volume 9 

Publisher: PeerJ

Authors: Sanjeev KumarRavendra SinghMohammad Zubair KhanAbdulfattah Noorwali

Keywords: Sentiment analysisConcept driftAdaptive ensembleData stream miningDrift-detection

Meet one of the Author:

Sanjeev Kumar

Dr Sanjeev Kumar received his Ph.D. in Computer Science and Information Technology from M.J.P. Rohilkhand University, Bareilly, Uttar Pradesh, India. His research background encompasses Natural Language Processing, Machine Learning, Deep Learning, Ensemble Classifier, Optimization Algorithms, and Stream Mining. His ability to think critically and approach problems in a logical and analytical manner differentiates him from others.


Department of Computer Science and Information Technology, M.J.P. Rohilkhand University, Bareilly, Uttar Pradesh, India – Mahatma Jyotiba Phule Rohilkhand University (MJPRU) is a university located in the state of Uttar Pradesh, India. It has both affiliated and campus jurisdictions and is governed by the UP Universities Act, 1973. The university offers higher education and has a long history, dating back to the late 19th and early 20th centuries. It is considered a generation three university, with the universities established between 1857 and 1947 being considered generation one and two, respectively.

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