Compared　with　survey　polls,　social　media　can　yield　a　better　and　more　comprehensive　understanding　of　public　perceptions　of　special　topics　in　a　more　scientific　manner.　However,　despite　this　advantage,　there　seem　to　be　limited　investigations　into　the　challenges　in　social　media-based　public　opinion　analysis.　This　study　offers　an　understanding　of　the　challenges　in　this　field　and　some　corresponding　recommendations.　Through　a　systematic　literature　review,　we　identify　54　papers　to　analyze　and　discuss　issues　related　to　data　collection,　data　quality,　and　data　mining.　This　paper　summarizes　a　framework　for　social　media-based　public　opinion　analysis　as　well　as　the　commonly　employed　data　mining　methodologies.　We　found　that　collecting　public　opinion　data　from　Facebook　and　Weibo　is　difficult　because　of　their　restricted　application　programming　interface　and　measures　against　Web　Crawler.　How　to　effectively　and　conveniently　delete　invalid　data　and　how　to　design　data　mining　methods　for　social　media　data,　especially　for　those　in　Chinese,　are　still　two　main　challenges　in　social　media-based　public　opinion　analysis.　We　claim　that　using　multiple　data　sources,　optimizing　keyword　settings,　enhancing　interdisciplinary　cooperation,　and　paying　more　attention　to　the　functional　role　of　social　media　can　benefit　the　development　of　social　media-based　public　opinion　analysis.　This　study　also　highlights　the　potential　risks　of　releasing　the　personal　information　of　the　public　in　the　use　of　social　media　data　in　research.