Comparative analysis of Mechanisms for Categorization and Moderation of User Generated Text Contents on a Social E-Governance Forum

Imeobong Frank Inyang, Simeon Ozuomba, Chinedu Pascal Ezenkwu

Abstract


This paper presents a comparative analysis of two mechanisms for an automated categorization and moderation of User Generated Text Contents (UGTCs) on a social e-governance forum. Posts on the forum are categorized into “relevant”, “irrelevant but interesting” and “must be removed”. Relevant posts are those posts that are capable of supporting government decisions; irrelevant but interesting category consists of posts that are not relevant but can entertain or enlighten other users; must be removed posts consists of abusive or obscene posts. Two classifiers, Support Vector Machine (SVM) with One-Vs-The-Rest technique and Multinomial Naive Bayes were trained, evaluated and compared using Scikit-learn. The results show that SVM with an accuracy score of 96% on test set performs better than Naive Bayes with 88.6% accuracy score on the same test set.

Keywords


Moderation; Ranking; UGC; UGTC; web 2.0; Sentiment analysis; Social e-governance

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References


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