In our application of the state-of-art system to talks, we could not achieve a comparable performance to the reported performance of the system. We investigated the potential reasons for the performance difference through further analysis. Some humor classification studies (Mihalcea and Strapparava, 2005;<cite> Yang et al., 2015</cite>; Barbieri and Saggion, 2014 ) have used negative instances from different domains or topics, because non-humorous sentences could not be found or are very challenging to collect in target domains or topics. Their studies showed that it was possible to achieve promising performance using data from heterogeneous domains. However, our study showed that humorous sentences which were semantically close to non-humorous sentences were very challenging to distinguish.