Social media, especially Twitter is now one of the most popular platforms where people can freely
express their opinion. However, it is difficult to extract important summary information from many millions
of tweets sent every hour. In this work we propose a new concept, sentimental causal rules, and techniques
for extracting sentimental causal rules from textual data sources such as Twitter which combine
sentiment analysis and causal rule discovery. Sentiment analysis refers to the task of extracting public
sentiment from textual data. The value in sentiment analysis lies in its ability to reflect popularly voiced
perceptions that are stated in natural language. Causal rules on the other hand indicate associations
between different concepts in a context where one (or several concepts) cause(s) the other(s). We believe
that sentimental causal rules are an effective summarization mechanism that combine causal relations
among different aspects extracted from textual data as well as the sentiment embedded in these causal
relationships. In order to show the effectiveness of sentimental causal rules, we have conducted experiments
on Twitter data collected on the Kurdish political issue in Turkey which has been an ongoing
heated public debate for many years. Our experiments on Twitter data show that sentimental causal rule
discovery is an effective method to summarize information about important aspects of an issue in Twitter
which may further be used by politicians for better policy making.