Automatic Detection of Double Meaning in Texts from the Social Networks

The paper presents a method for automatic detection of double meaning of texts in English from the social networks. For the purposes of this paper we define double meaning as one of irony, sarcasm and satire. We proposed nine rules selected from a pool of twenty. We defined six features and evaluated their predictive accuracy. Further we compared the accuracy of three different classifiers - Naive Bayes, k-Nearest Neighbours and Support Vector Machine. We also studied the predictive accuracy of all words and bi-terms. We test the algorithms above against opinions from the social network: sample opinions from the social networks Facebook, Twitter and Google+. These opinions were extracted via HTTP requests using one of the hashtags #sarcasm, #irony or #satire and we select 3000 opinions for each of the tests.