The　social　network　allows　individuals　to　create　public　and　semi-public　web-based　profiles　to　communicate　with　other　users　in　the　network　and　online　interaction　sources.　Social　media　sites　such　as　Facebook,　Twitter,　etc.,　are　prime　examples　of　the　social　network,　which　enable　people　to　express　their　ideas,　suggestions,　views,　and　opinions　about　a　particular　product,　service,　political　entity,　and　affairs.　This　research　introduces　a　Machine　Learning-based　(ML-based)　classification　scheme　for　analyzing　the　social　network　reviews　of　Yemeni　people　using　data　mining　techniques.　A　constructed　dataset　consisting　of　2000　MSA　and　Yemeni　dialects　records　used　for　training　and　testing　purposes　along　with　a　test　dataset　consisting　of　300　Modern　Standard　Arabic　(MSA)　and　Yemeni　dialects　records　used　to　demonstrate　the　capacity　of　our　scheme.　Four　supervised　machine　learning　algorithms　were　applied　and　a　comparison　was　made　of　performance　algorithms　based　on　Accuracy,　Recall,　Precision　and　F-measure.　The　results　show　that　the　Support　Vector　Machine　algorithm　outperformed　the　others　in　terms　of　Accuracy　on　both　training　and　testing　datasets　with　90.65%　and　90.00,　respectively.　It　is　further　noted　that　the　accuracy　of　the　selected　algorithms　was　influenced　by　noisy　and　sarcastic　opinions.