CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification
Raj Kumar Gupta, Yinping Yang · 2017
This paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017.A key feature of our system is the embedded ability to detect sarcasm in order to enhance the performance of sentiment classification.We first constructed an affect-cognition-sociolinguistics sarcasm features model and trained a SVM-based classifier for detecting sarcastic expressions from general tweets.For sentiment prediction, we developed CrystalNest-a two-level cascade classification system using features combining sarcasm score derived from our sarcasm classifier, sentiment scores from Alchemy, NRC lexicon, n-grams, word embedding vectors, and part-of-speech features.We found that the sarcasm detection derived features consistently benefited key sentiment analysis evaluation metrics, in different degrees, across four subtasks A-D.