For example, in Table 4, the topic generated by the TOPIC+DISC+REL model contains sentiment words like "wrong" and "hate."
a direct use of the topic representations to extract summaries will unavoidably select messages that mostly reflect sentimental component, which is also illustrated by the case study in Section 5.3.