Online business intelligence systems often collect the texts from different sources, such as social media and news websites that can be heterogeneous in practice. These collections bring the difficulties of managing and organizing the comprehensive information hidden in different texts of the system. To more effectively organize the multisourced texts and help online users acquire wider knowledge, we propose a business intelligence system which integrates the multisourced texts from multisources. Regarding in many occasions, multisourced texts share some common contents with respect to the same topics. For example, a tweet and a news report may talk about the same event. Therefore, our goal is to correlate such texts of different sources with respect to the similar topics and get integrated more comprehensive information to facilitate other data mining tasks as well as online applications. To handle the problem, we propose a heterogeneous information network-based text aligning (HINTA) framework in this paper. HINTA applies meta-paths to calculate the text similarities, and constructs correlated pairs between the two types of texts. Next, HINTA first applies anchored pairs as bridges to combine the different types of texts. Finally, three different inference methods are employed to align the multisourced texts. Experimental results on real-world dataset show the effectiveness and efficiency of the framework in addressing the texts alignment problem.