Sentimental Causal Rule Discovery from Twitter. Dehkharghani, R.; Mercan, H.; Javeed, A.; and Saygın, Y. In Expert Systems with Applications, to appear.
abstract   bibtex   
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 di*erent concepts in a context where one (or several concepts) cause(s) the other(s). We believe that sentimental causal rules are an e*ective summarization mechanism that combine causal relations among di*erent aspects extracted from textual data as well as the sentiment embedded in these causal relationships. In order to show the e*ectiveness 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 e*ective method to summarize information about important aspects of an issue in Twitter which may further be used by politicians for better policy making.
@inproceedings{ dehkharghani2014sentimental,
  title = {Sentimental Causal Rule Discovery from Twitter},
  booktitle = {Expert Systems with Applications},
  author = {Rahim Dehkharghani and 
  			Hanefi Mercan and
  			Arsalan Javeed and  
  			Yücel Sayg{ı}n},
  year = {to appear},
  abstract = { 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 di*erent concepts in a context where one (or several concepts) cause(s) the other(s). We believe that sentimental causal rules are an e*ective summarization mechanism that combine causal relations among di*erent aspects extracted from textual data as well as the sentiment embedded in these causal relationships. In order to show the e*ectiveness 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 e*ective method to summarize information about important aspects of an issue in Twitter which may further be used by politicians for better policy making.}
}
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