Submission Site: https://easychair.org/conferences/?conf=ntcir17
Although argument mining has been discussed for several years, financial argument mining is still in the early stage. In FinNum-3, we proposed the concept for identifying the arguments in financial narratives. To perform a more fine-grained analysis, we propose an argument-based sentiment analysis task in FinArg-1. The idea is based on the concept that good news may not always lead to a bullish claim. In this task, we separate the analyst report and earnings conference calls into two parts: premise and claim, and further label the sentiment toward the argument. For the premise, the sentiment labels are positive/neutral/negative. For the claim, the sentiment labels are bullish/neutral/bearish. In this way, we can better understand the argumentation structure in professional reports and managers' presentations.
On the other hand, the other task aims to identify the attack and support argumentative relationships in the social media discussion thread. Instead of analyzing a single social media post, we consider the whole discussion thread. In this task, we attempt to link the posts with attack and support labels. With these labels, we can understand the argumentation structure among opinions. FinArg-1 could also lead our community to discuss more fine-grained information embedded in the financial documents and discussions.