SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron

Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 633–637 New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron Rajalakshmi S, Angel Deborah S, S Milton Rajendram, Mirnalinee T T SSN College of Engineering Chennai 603 110, Tamil Nadu, India rajalakshmis@ssn.edu.in, angeldeborahs@ssn.edu.in miltonrs@ssn.edu.in, mirnalineett@ssn.edu.in Abstract Sentiment analysis plays an important role in E-commerce. Identifying ironic and sarcastic content in text plays a vital role in inferring the actual intention of the user, and is necessary to increase the accuracy of sentiment analy- sis. This paper describes the work on iden- tifying the irony level in twitter texts. The system developed by the SSN MLRG1 team in SemEval-2018 for task 3 (irony detection) uses rule based approach for feature selection and MultiLayer Perceptron (MLP) technique to build the model for multiclass irony classi- fication subtask, which classifies the given text into one of the four class labels. 1 Introduction Humans have the natural ability to identify the sentiment or the irony intended in a review or com- ment. However, identifying the intention of the user is a difficult task for the machine. Detecting irony present in a text is critical to sentiment anal- ysis since it will inverse the polarity of the senti- ment inferred (Hernandez-Farias et al., 2015). Choice of shops, books, movies, hotels and var- ious other services and products is influenced by comments and reviews in social media to a large extent. Huge amount of data is available in the Internet about the choices people make and their reviews about it. Irony in texts affects the polarity of the senti- ment inferred from them. Since it gives the text a meaning that is just the opposite to what is actu- ally said, it is called as a polarity reverser (Farias et al., 2016). Irony is studied in various disci- plines such as linguistics, philosophy and psychol- ogy. Due to the frequent use of irony in social media, its detection has gained importance in nat- ural language processing, which faces difficulty in achieving a high performance (Liu, 2012; Wal- lace, 2015). The potential applications of irony detection include text mining, author profiling, de- tecting online harassment and sentiment analysis (Van Hee et al., June 2018). SSN MLRG1 team has already worked in sentiment analysis tasks conducted in SemEval 2017 (Angel Deborah et al., 2017a,b). We can identify three types irony namely verbal irony, situational irony and dramatic irony. Sub- task B in task 3 is a multiclass classification task for classifying a given tweet to one of these four classes: 1. verbal irony realized through a polarity con- trast, 2. verbal irony without such a polarity contrast, 3. situational irony, and 4. non-irony. 2 Related Work Unlike factual information, sentiment analysis and opinion mining have to deal with subjective infor- mation. Consequently, for any problem, it is im- portant to analyze opinions collected from many people and summarize them. Social and …

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