Extracting Ironic Tweets using Experts Model

Posts on Twitter allow users to express ideas and opinions very dynamically. This high volume of data provides relevant clues about the public judgment on a specific product, event, service, etc. While traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level (very positive, low negative, and so on) and cannot exploit the intensity information. Recently, the problem of irony detection in social media has been proven to be pervasive among research enthusiasts, posing a challenge to sentiment analysis systems. Moreover, the figurative use of language has received scarce attention from the computational linguistic research point of view. This paper proposes an architecture, the Experts Model, inspired by the standard Mixture of Experts (MoE) model. The key idea here is that each expert learns different sets of features from the feature vector, which helps in better irony detection (Ironic vs. Non-ironic - SemEval-2018 subtask A) and ironic type detection (Verbal irony with vs. without polarity contrast vs. Situational irony vs. Non-irony - SemEval-2018 subtask B) from the tweet. We compared our Experts Model’s results with baseline results along with the top five performers of SemEval-2018 Task-3, Ironic detection. The experimental results show that our proposed approach deals with the ironic detection problem and stands at the top-3 results. We opted for a transfer learning approach by applying our proposed model on three different datasets #ironic, #sarcasm, and #humor, and we achieved a better F1-score.