With　the　rapid　development　of　the　Internet　and　big　data　technologies,　a　rich　of　online　data　(including　news　releases)　can　helpfully　facilitate　forecasting　oil　price　trends.　Accordingly,　this　study　introduces　sentiment　analysis,　a　useful　big　data　analysis　tool,　to　understand　the　relevant　information　of　online　news　articles　and　formulate　an　oil　price　trend　prediction　method　with　sentiment.　Three　main　steps　are　included　in　the　proposed　method,　i.e.,　sentiment　analysis,　relationship　investigation　and　trend　prediction.　In　sentiment　analysis,　the　sentiment　(or　tone)　is　extracted　based　on　a　dictionary-based　approach　to　capture　the　relevant　online　information　concerning　oil　markets　and　the　driving　factors.　In　relationship　investigation,　the　Granger　causality　analysis　is　conducted　to　explore　whether　and　how　the　sentiment　impacts　oil　price.　In　trend　prediction,　the　sentiment　is　used　as　an　important　independent　variable,　and　some　popular　forecasting　models,　e.g.,　logistic　regression,　support　vector　machine,　decision　tree　and　back　propagation　neural　network,　are　performed.　With　crude　oil　futures　prices　of　the　West　Texas　Intermediate　(WTI)　and　news　articles　of　the　Thomson　Reuters　as　studying　samples,　the　empirical　results　statistically　support　the　powerful　predictive　power　of　sentiment　for　oil　price　trends　and　hence　the　effectiveness　of　the　proposed　method.