Twitter Sarcasm Detection Exploiting a Context-Based Model

Wang, Zelin; Wu, Zhijian; Wang, Ruimin; Ren, Yafeng

doi:10.1007/978-3-319-26190-4_6

Cited by 62 publications

(35 citation statements)

References 12 publications

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“…Manual: [Riloff et al 2013;Maynard and Greenwood 2014;Ptácek et al 2014;Abhijit Mishra and Bhattacharyya 2016;Abercrombie and Hovy 2016] Hashtag-based: González-Ibánez et al 2011;Reyes et al 2013;Barbieri et al 2014a;Ghosh et al 2015b;Bharti et al 2015;Liebrecht et al 2013;Bouazizi and Ohtsuki 2015a;Wang et al 2015;Barbieri et al 2014b;Bamman and Smith 2015;Fersini et al 2015;Khattri et al 2015;Rajadesingan et al 2015 [Lukin and Walker 2013;Reyes and Rosso 2014;Buschmeier et al 2014;Liu et al 2014;Filatova 2012] Other datasets [Tepperman et al 2006;Kreuz and Caucci 2007;Veale and Hao 2010;Rakov and Rosenberg 2013;Ghosh et al 2015a;Joshi et al 2016a;Abercrombie and Hovy 2016]…”

Section: Text Form Related Work Tweetsmentioning

confidence: 99%

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Automatic Sarcasm Detection

2017

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Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, sarcasm detection has witnessed great interest from the sentiment analysis community. This paper is the first known compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and use of context beyond target text. In this paper, we describe datasets, approaches, trends and issues in sarcasm detection. We also discuss representative performance values, shared tasks and pointers to future work, as given in prior works. In terms of resources that could be useful for understanding state-of-the-art, the survey presents several useful illustrations -most prominently, a table that summarizes past papers along different dimensions such as features,

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Section: Text Form Related Work Tweetsmentioning

confidence: 99%

“…Bamman and Smith [2015] use binary logistic regression. Wang et al [2015] use SVM-HMM in order to incorporate sequence nature of output labels in a conversation. Liu et al [2014] compare several classification approaches including bagging, boosting, etc.…”

Section: Learning Algorithmsmentioning

confidence: 99%

Automatic Sarcasm Detection

2017

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“…For both corpora adding 'pre" and "post" messages do not seem to affect significantly the F1 scores, even though using the "post" message as context seems to improve for the sarcastic class (Oraby et al 2017). Unlike the above approaches that model the utterance and context together, Wang et al (2015) and Joshi et al (2016a) use a sequence labeling approach and show that conversation helps in sarcasm detection. Inspired by this idea of modeling the current turn and context separately, in our prior work (Ghosh, Fabbri, and Muresan 2017) -which this paper substantially extends -, we proposed a deep learning architecture based on LSTMs, where one LSTM reads the context (prior turn) and one LSTM reads the current turn, and showed that this type of architecture outperforms a simple LSTM that just reads the current turn.…”

Section: Related Workmentioning

confidence: 99%

“…To build the conversation context, for each sarcastic and non-sarcastic tweet we used the "reply to status" parameter in the tweet to determine whether it was in reply to a previous tweet: if so, we downloaded the last tweet (i.e., "local conversation context") to which the original tweet was replying to (Bamman and Smith 2015). In addition, we also collected the entire threaded conversation when available (Wang et al 2015). Although we have collected over 200K tweets in the first step, around 13% of them were a reply to another tweet, and thus our final Twitter conversations set contains 25,991 instances (12,215 instances for sarcastic class and 13,776 instances for the non-sarcastic class).…”

Section: Datamentioning

confidence: 99%

Sarcasm Analysis Using Conversation Context

Ghosh

Fabbri

Muresan

2018

Computational Linguistics

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Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker's sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection; (2) can we identify what part of conversation context triggered the sarcastic reply; and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic. To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network (Rocktäschel et al. 2016), outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these datasets ranging from their size to the nature of the gold-label annotations. To address the last two issues, we present a qualitative analysis of the attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.

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“…The work closest to ours is by Wang et al (2015). They use a labeled dataset of 1500 tweets, the labels for which are obtained automatically.…”

Section: Performance On Features Reported In Prior Workmentioning

confidence: 99%

Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series `Friends'

Joshi¹,

Tripathi²,

Bhattacharyya³

et al. 2016

Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning

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This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a sequence of utterances. We create a manuallylabeled dataset of dialogue from TV series 'Friends' annotated with sarcasm. Our goal is to predict sarcasm in each utterance, using sequential nature of a scene. We show performance gain using sequence labeling as compared to classification-based approaches.Our experiments are based on three sets of features, one is derived from information in our dataset, the other two are from past works. Two sequence labeling algorithms (SVM-HMM and SEARN) outperform three classification algorithms (SVM, Naive Bayes) for all these feature sets, with an increase in F-score of around 4%. Our observations highlight the viability of sequence labeling techniques for sarcasm detection of dialogue.

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Twitter Sarcasm Detection Exploiting a Context-Based Model

Cited by 62 publications

References 12 publications

Automatic Sarcasm Detection

Automatic Sarcasm Detection

Sarcasm Analysis Using Conversation Context

Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series `Friends'

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