Recently, many people express their opinions using social networking services such as Twitter and Facebook. Each opinion has a stance related to something such as product, service, and politics. The task of detecting a stance is known as sentiment analysis, reputation mining, and stance detection. A popular approach for stance detection uses sentiment polarity towards a target in a text. This approach is known as targeted sentiment analysis. If a target appears in text, the detecting stance based on targeted sentiment polarity would work well. However, how can we detect stance towards an event? (e.g. 'I cannot understand why man can marry only with a woman', 'The problem of low birth rate becomes more severe' to the event 'Allowing same-sex marriage'). To detect these stances, it is necessary to recognize a situation in which the event occurs or does not occur. To classify texts including these phenomena, we propose a classification method based on machine learning considering PRIOR-SITUATION and EFFECT.