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Mohanad Faeq Ali
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Sentimental classification analysis of polarity multi-view textual data using data mining techniques Ali Hameed Yassir; Ali A. Mohammed; Adel Abdul-Jabbar Alkhazraji; Mustafa Emad Hameed; Mohammed Saad Talib; Mohanad Faeq Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (577.994 KB) | DOI: 10.11591/ijece.v10i5.pp5526-5534

Abstract

The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
Co-Authors Adel Abdul-Jabbar Alkhazraji Ali A. Mohammed Ali Hameed Yassir Mohammed Saad Talib Mustafa Emad Hameed