Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.

By Diana Maynard, M. Greenwood. International Conference on Language Resources and Evaluation, 2014

422 citations recorded by the source corpus.

Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans too. It has an important effect on sentiment, but is usually ignored in social media analysis, because it is considered too tricky to handle. While there exist a few systems which can detect sarcasm, almost no work has been carried out on studying the effect that sarcasm has on sentiment in tweets, and on incorporating this into automatic tools for sentiment analysis. We perform an analysis of the effect of sarcasm scope on the polarity of tweets, and have compiled a number of rules which enable us to improve the accuracy of sentiment analysis when sarcasm is known to be present. We consider in particular the effect of sentiment and sarcasm contained in hashtags, and have developed a hashtag tokeniser for GATE, so that sentiment and sarcasm found within hashtags can be detected more easily. According to our experiments, the hashtag tokenisation achieves 98% Precision, while the sarcasm detection achieved 91% Precision and polarity detection 80%.

Subfields: Artificial Intelligence, Social Psychology

View Paper: https://www.semanticscholar.org/paper/bdf94c10cac57180703ba7f87c70fcac5fa4e3d4

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Citations in the corpus

Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, background): “[117] report that agreement on sarcasm amongst annotators working in English is low, something echoed by annotations of Danish content [118].”
Offensive Language and Hate Speech Detection for Danish (2019, background): “Top-level features often require predictive words to occur in both the training set and the test sets, as discussed in Schmidt and Wiegand (2017), but unseen terms prevail in noisy text (Derczynski et al.”
Like It or Not (2016, background): “Maynard and Greenwood [2014] investigated the use of sarcasm in tweets and studied its impact on TSA.”
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks (2016, background): “…and the use of emoti-cons (Gonz´alez-Ib´anez et al., 2011; Carvalho et al., 2009; Barbieri et al., 2014), unsupervised pattern mining approach (Maynard and Greenwood, 2014), semi-supervised approach (Riloff et al., 2013) and n-grams based approach (Tsur et al., 2010; Davidov et al., 2010;…”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “[75] report that agreement on sarcasm amongst annotators working in English is low, something echoed by annotations of Danish content [34].”
Harnessing Context Incongruity for Sarcasm Detection (2015, positive): “Our system also outperforms two past works (Riloff et al., 2013; Maynard and Greenwood, 2014) with 10-20% improvement in F-score.”
SemEval-2018 Task 3: Irony Detection in English Tweets (2018, background): “In fact, it has been demonstrated that several applications struggle to maintain high performance when applied to ironic text (e.g. Liu, 2012; Maynard and Greenwood, 2014; Ghosh and Veale, 2016).”