Sentimental causal rule discovery from Twitter

Sentiment analysis refers to the task of extracting the sentiment of people from textual data. This analysis can be employed to reflect the public’s ideas about an issue stated in natural language text. On the other hand, causal rules are associations between different concepts in a context that one (or several concepts) cause(s) the other(s). In this paper, we propose a new concept ”sentimental causal rules” which incorporate causal relationships among different aspects extracted from textual data and the sentiment towards those aspects. We also describe techniques to extract sentimental causal rules and show their effectiveness on Twitter as the data resource. As a case study, we investigated about the Kurdish issue in Turkey which is very important topic in our region causing heated debates that the governments need to follow. The experiments on Twitter data on the chosen case study suggest that sentimental causal rules are an effective way to summarize important aspects in textual data and to suggest causal relationships among those aspects which may lead to better policy making.