Collaboration wise, logistics naturally implies the concurrency of a set of companies being part of products and services value chain. However, collaborative logistics imposes new challenges relating to effective decision making process involving planning, scheduling, control, and coordination at different companies in the logistics network. These decision making processes require the effective capability to capture, process, and analyze information of processes, partners, environment, regulations, etc. That information is, of course, dynamic, and it is available in a scattered way all through a variety of sources, periodicity, and formats. Effective and efficient tools for capturing, classifying, processing, and report useful information for supporting collaborative logistics decision making processes are required. Large amounts of digital text available on the web contain useful information for enabling collaborative logistics. The amount of digital texts it is expected to increase significantly in the near future, making the development of data analysis applications an urgent need. Information gathering has been traditionally faced integrating systems and databases at the various institutions and companies participating in the logistics network. An alternative approach is using data available in the web as input to automatic text classifiers implemented with machine learning techniques.