This study reports a semantic analysis of cancer-related conversation in Twitter during a 16- day period. More than 2.69 million tweets related to cancer were collected. Taxonomy consisting of 223 cancer-related key terms were created and developed. More than 1.13 million tweets filtered with the taxonomy were analyzed and visualized, in terms of the frequency, periodicity, co-occurrence and sentiments. Findings report (1) the most visible keywords, which partially illustrate the topics and message relevant to cancer, detectable from social streaming in Twitter; (2) a two-day-of-week rhythm with frequency of cancer-related tweets, which was highly influenced by breaking news or news events; (3) the key terms co-occurrence in tweets concerning breast cancer, lung cancer and prostate cancer, and (4) a sentiment network that comprises both positive and negative feelings or concerns about cancer. The potential theoretical contributions of this project and its practical implications are also discussed.