An LSTM -Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data (2019)

Abstract: Self-deprecating sarcasm is a special category of sarcasm, which is nowadays popular and useful for many real-life applications, such as brand endorsement, product campaign, digital marketing, and advertisement. The self-deprecating style of campaign and marketing strategy is mainly adopted to excel brand endorsement and product sales value. In this paper, we propose an LSTM-based deep learning approach for detecting self-deprecating sarcasm in textual data. To the best of our knowledge, there is no prior work related to self-deprecating sarcasm detection using deep learning techniques. Starting with a filtering step to identify self-referential tweets, the proposed approach adopts a deep learning model using LSTM for detecting self-deprecating sarcasm. The proposed approach is evaluated over three Twitter datasets and performs significantly better in terms of precision, recall, and f-score.

## introduction over a decade, the popularity of the microblogging platform, twitter, has significantly increased for analyzing its content for varied realworld applications. the information extracted from twitter can shed light on numerous applications, such as text categorization, sentiment analysis, election campaign and result prediction, opensource intelligence, and event detection. however, the contents available on twitter in the form of tweets are short and limited to maximum 280 characters. moreover, tweets are informal and mainly consist of misspelled words, slangs, bashes, acronyms, shortened words, nonliteral unstructured phrases, and emoticons. due to existence of such volumunious informal texts in the form of tweets text information processing is a challenging task. moreover, analysis of the tweets has become more challenging due to presence of figurative language, especially sarcasm. the main role of a sarcastic tweet is to reverse the actual polarity and alter the literal semantics. however, the computational detection of sarcasm benefits many applications, especially opinion mining and sentiment analysis systems @xcite . the online macmillan dictionary defines sarcasm 1 as "the activity of saying or writing the opposite of what you mean, or of speaking in a way intended to make someone else feel stupid or show them that you are angry". sarcasm is the most seen figurative language category over online social media platforms. the presence of sarcasm in tweets is dramatically rising and computational detection of sarcasm is a challenging and interesting task. it is widely covered by researchers in recent years, but the study on different categories 2 of sarcasm, such as self-deprecating sarcasm, is very limited. self-deprecating sarcasm 3 is a special category of sarcasm in which users mainly apply sarcasm over themselves using disparage, ridicule, and contemptuous remarks in a sarcastic style using humor. it is defined as a "sarcasm that plays off of an exaggerated sense of worthlessness and inferiority". for example, the phrase love going to the office on sunday in the text "really, i always love going to the office on sunday" represents a self-deprecating sarcasm. nowadays, self-deprecating sarcasm has become a new style of product marketing and campaign strategy. it is mainly used for product endorsement purposes. this new marketing and campaign strategy is mainly used to excel the busi- in this paper, we propose a deep learning approach using long short-term memory (lstm) to detect self-deprecating sarcasm in textual data like tweets. initially, after preprocessing, we first identify self-referential tweets from the dataset based on a set of patterns, and rest of tweets are filtered out. the main motivation behind the filtration of the non-self-referential tweets is to increases the overall efficiency of the selfdeprecating sarcasm detection process. in brief, the main role of the self-referential tweets identification module can be summarized as follows: once the list of self-referential tweets is generated, it is passed to the model learning and classification module for self-deprecating sarcasm detection. to this end, each self-referential tweet is converted into an input vector, it is fed to pretrained glove word embedding, and model learning and classification task is accomplished using lstm for detecting self-deprecating sarcasm. this remainder of this paper is organized as follows. section 2 presents a brief review of the state-of-the-art techniques and approaches for computational sarcasm detection. it also highlights the uniqueness of our proposed approach over the existing state-of-the-art techniques. section 3 presents the functional details of the proposed approach, including model learning and classification using lstm. section 4 presents the experimental and evaluation results. finally, section 5 concludes the paper and discusses future research directions. ## related work automatic sarcasm detection is considered as a classification task @xcite , and the main task is to classify any piece of texts as sarcasm or non-sarcasm. @xcite applied semi-supervised approach to detect sarcasm in amazon product reviews. @xcite applied the same approach to detect sarcasm in tweets and product reviews. @xcite considered lexical and pragmatics features to detect sarcasm on twitter datasets. @xcite identified sarcastic contrastbased patterns and considered words with positive sentiment and negative phrases in a tweet containing sarcasm. @xcite discussed the role of hyperrbole in sarcasm detection. @xcite detected sarcasm in english and czech tweets. @xcite proposed rule-based algorithms based on some patterns for sarcasm detection. they also highlighted the importance of hyperbole in sarcastic texts. @xcite extracted extra-linguistic information based on the context of the instances for sarcasm detection. @xcite applied three machine learning classifiers -support vector machine (svm), logistic regression, and decision tree for sarcasm detection, considering the behavioral modeling-based approach. @xcite proposed @xcite and considered sarcasm, irony, and metaphor for sentiment analysis in twitter data. @xcite discussed the role of incongruity for sarcasm detection. bouazizi and ohtsuki (2016) considered a pattern-based approach. @xcite considered lexicaland contextual-based features. @xcite proposed word-embedding related features using word2vec foot_1 . recently, deep learning models have been used as a popular technique for sarcasm detection problem. @xcite applied a bi-directional gated recurrent neural network for sarcasm detection. they considered syntactic and semantic information and extracted contextual features. amir et al. (2016) though sarcasm detection is widely covered by the researchers, studies related to the varied categories of sarcasm are still not explored. recently, @xcite noticed the use of self-deprecating sarcasm in twitter, mainly for the purpose of brand endorsement and sales campaign. they considered self-deprecating sarcasm as a special category of sarcasm in which users express sarcasm over themselves. they also proposed a rule-based and machine learning-based approach for detecting self-deprecating sarcasm detection in twitter. the proposed work in this paper is new lstm-based deep learning approach for self-deprecating sarcasm detection in textual data. ## proposed approach in this section, we discuss the proposed lstmbased deep learning approach for self-deprecating sarcasm detection. figure 1 presents the workflow of the proposed approach. it can be seen from this figure that besides data crawling and data pre-processing, the main functionalities of the proposed approach are self-referential tweets detection, and self-deprecating sarcasm detection using deep learning technique. further details about all functional modules are presented in the following sub-sections. ## experiments setup and results in this section, we discuss the experimental evaluation of the proposed approach. ## conclusion and future work in this paper, we have proposed a new approach using lstm-based deep learning for detecting self-deprecating sarcasm in textual data. the self-deprecating sarcasm is a special category of sarcasm in which users apply sarcasm on themselves. one of the major applications of this work is to promote self-deprecating marketing strategies. the proposed approach is evaluated over three twitter datasets, including two benchmark datasets, and the experimental results are promising. it also performs significantly better than one of the state-of-the-art methods, which used rulebased and machine learning techniques for selfdeprecating sarcasm detection. exploring new patterns and consideration of multimedia contents for self-deprecating sarcasm detection seems one of the promising directions of future research.