AOM: A New Task for Agitative Opinion Mining in We-media

As We-media continues to develop, there is a concerning rise of agitative opinions on We-media platforms, which usually lead to online violence. To formalize the research on identifying whether a sentence contains agitative opinions, we propose a new task AOM for agitative opinion mining in We-media. To clarify the task, we make a clear definition to agitative opinions in which they are categorized into nine types and we manually construct a ten-thousands scale Chinese agitative opinion dataset CAOD based on WeChat public account, for research purpose. Furthermore, a baseline model \(\mathrm{CAOD_{MINER}}\) based on TextCNN is proposed and sampling methods are adopted in training it. For comparison, we also apply several mainstream text classifiers into CAOD. The comparative experiment and further analysis show that AOM is a soluble but challenging task, where unbalanced data distribution, diversity of expression forms, context dependency, scarcity of external knowledge and implicit expression deserve to be studied in the future.