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

《Mathematical and Software Engineering》 3卷1期 (2017/03) Pp. 78-86

摘要

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.

關鍵字

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