Abstract
Sarcasm is a form of communication where the individual states the opposite of what is implied. Therefore, detecting a sarcastic tone is somewhat complicated due to its ambiguous nature. On the other hand, identification of sarcasm is vital to various natural language processing tasks such as sentiment analysis and text summarisation. However, research on sarcasm detection in Persian is very limited. This paper investigated the sarcasm detection technique on Persian tweets by combining deep learning-based and machine learning-based approaches. Four sets of features that cover different types of sarcasm were proposed, namely deep polarity, sentiment, part of speech, and punctuation features. These features were utilised to classify the tweets as sarcastic and nonsarcastic. In this study, the deep polarity feature was proposed by conducting a sentiment analysis using deep neural network architecture. In addition, to extract the sentiment feature, a Persian sentiment dictionary was developed, which consisted of four sentiment categories. The study also used a new Persian proverb dictionary in the preparation step to enhance the accuracy of the proposed model. The performance of the model is analysed using several
Highlights
Twitter has become one of the biggest destinations for people to put forward their opinions
Four sets of features were extracted, namely (1) Sentiment Feature, (2) Deep Polarity Feature, (3) POS Feature, and (4) Punctuation Feature. These features were extracted in a way that covered different types of Persian sarcasm
This study proposed a method to detect Persian sarcasm based on deep learning and machine learning for the first time
Summary
Twitter has become one of the biggest destinations for people to put forward their opinions. “I’m very pleased to waste my four hours on such a pathetic movie!” This tweet has the word “pleased” with a positive sentiment, the whole emotion of the tweet is negative. In Persian, people tend to use sarcasm in their daily conversations for criticizing and censoring especially in political topics (Hokmi, 2017).To the best of the researchers’ knowledge, there is no work on sarcasm detection in Persian. They aim to present a model that performs the task of sarcasm detection in Persian. The results are shown in the fourth section, while the fifth section concludes this work and proposes possible directions for future works
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