Sentiment analysis technology has made it possible to precisely calculate the daily reactions and opinions of investors, which has been found to have a significant influence on financial asset pricing. Thus, in this study, we examine the impacts that predictive power investor sentiment has over the price of China's crude oil. We first constructed investor sentiment indexes of China's crude oil futures based on specific economic variables and comments found on one of the most active online financial forums. Then, five popular machine learning tools were utilized to generate predictions. According to our findings, the long short-term memory model combined with the composite sentiment index performed the best due to a lower rate of prediction errors and greater directional accuracy for time-series forecasting of one-day-ahead prices. In this way, this study could aid researchers to more effectively investigate the energy sector which is rapidly changing and highly speculative in nature Display omitted •We investigate whether investor sentiment indicators can help predict the price of China's crude oil futures.•We crawled comments from financial forums and performed sentiment analysis using APIs from three Internet companies.•The LSTM model containing the sentiment index constructed by voting methods is the most accurate forecasting method.•Our results can provide useful implications for both investors and policy regulators.