Sports visualization focuses on the use of structured data, such as box-score data and tracking data. Unstructured data sources pertaining to sports are available in various places such as blogs, social media posts, and online news articles. Sports visualization methods either not fully exploited the information present in these sources or the proposed visualizations through the use of these sources did not augment to the body of sports visualization methods. We propose the use of unstructured data, namely cricket short text commentary for visualization. The short text commentary data is used for constructing individual player's strength rules and weakness rules. A computationally feasible definition for player's strength rule and weakness rule is proposed. A visualization method for the constructed rules is presented. In addition, players having similar strength rules or weakness rules is computed and visualized. We demonstrate the usefulness of short text commentary in visualization by analyzing the strengths and weaknesses of cricket players using more than one million text commentaries. We validate the constructed rules through two validation methods. The collected data, source code, and obtained results on more than 500 players are made publicly available.

In the data-driven future of project management, project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks. For example, many organisations require project managers to provide regular project status updates as part of the delivery assurance process. These updates typically consist of text commentary and an associated red-amber-green (RAG) status, where red indicates a failing project, amber an at-risk project and green an on-track project. Wouldn't it be great if we could automate this process, making it more consistent and objective? In this post I will describe how we can achieve exactly that by applying natural language processing (NLP) to automatically classify text commentary as either red, amber or green status.