Towards an approach to detect irony and sarcasm in social media

Abstract

Social media analysis is an effective tool to keep track of what are the general public’s demands and opinions about all sorts of subjects ; however, when it comes to non literal language social media content can be interpreted into misleading and embarrassingly wrong information and that interpretation gets worse with ironic and sarcastic content. In this thesis we create an approach to detect sarcastic and ironic content in Twitter so that it will possess more attention from analysts than other content and therefore can be interpreted more delicately or manually if not possible ,the languages that we are interested in our detection are English and Arabic. In our approach we have focused on the linguistic features that previous researchers have discovered in addition to the normalization of the used language in terms of spelling and formalism . After performing tests on different artificial intelligence models we obtained encouraging results in terms of accuracy .We found that the ideal classifier for English sarcasm detection is Support vector machine SVM which gave 98.38% in terms of accuracy, for English irony detection the best classifier was logistic regression ; it gave 89.42% in terms of accuracy and finally for Arabic sarcasm detection we found that the best classifier was stochastic gradient descent and its accuracy was 86.16%,these results were encouraging due to the application of language normalization. After this step we have retained these models for a final web application that allows to analyze a tweet or a group of tweets and return whether they’re ironic/sarcastic or not. Key words: Social Media Analysis , Machine learning ,Twitter , Sarcasm detection ,Irony detection.