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Ensemble Method for Online Sentiment Classification Using Drift Detection-Based Adaptive Window Method

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Advances on Intelligent Informatics and Computing (IRICT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 127))

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Abstract

Textual data streams have been widely applied in real-world applications where online users’ expressed their opinions for online products. Mining this stream of data is a challenging task for researchers as a result of changes in data distribution, a phenomenon widely known as concept drift. Most of the existing classification methods incorporated drift detection methods that depend on the classification errors. However, these methods are prone to higher false-positive or missed detections rates. Thus, there is a need for more sensitive detection methods that can detect the maximum number of drifts in the data stream to improve classification accuracy. In this paper, we present a drift detection-based adaptive windowing for ensemble classifier, an adaptive unsupervised learning algorithm for sentiment classification, and opinion mining. The proposed algorithm employs four different dissimilarity measures to quantify the magnitude of concept drift in data streams, to improve the classification performance. Series of the experiments were conducted on the real-world datasets and the results demonstrated the efficiency of our proposed model.

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Author information

Authors and Affiliations

  1. School of Computing, Univerti Teknologi Malaysia, 81310, Bahru, Johor, Malaysia

    Idris Rabiu, Naomie Salim & Maged Nasser

  2. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  3. Computer Science and Information Systems, Applied College, Taibah University, Medina, Saudi Arabia

    Waseem Alromema

  4. Ibrahim Badamsi Babangida University, Lapai, Niger State, Nigeria

    Idris Rabiu, Aisha Awal & Elijah Joseph

  5. Baze University, Abuja, Nigeria

    Amit Mishra

Authors
  1. Idris Rabiu
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  2. Naomie Salim
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  3. Maged Nasser
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  4. Faisal Saeed
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  6. Aisha Awal
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  8. Amit Mishra
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Correspondence to Idris Rabiu .

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Editors and Affiliations

  1. Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Kedah, Malaysia

    Fathey Mohammed

  3. Department of Computer Science, School of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Fuad Ghaleb

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Rabiu, I. et al. (2022). Ensemble Method for Online Sentiment Classification Using Drift Detection-Based Adaptive Window Method. In: Saeed, F., Mohammed, F., Ghaleb, F. (eds) Advances on Intelligent Informatics and Computing. IRICT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 127. Springer, Cham. https://doi.org/10.1007/978-3-030-98741-1_11

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