Harnessing Context Incongruity for Sarcasm Detection
By Aditya Joshi, Vinita Sharma, Pushpak Bhattacharyya. Annual Meeting of the Association for Computational Linguistics, 2015
320 citations recorded by the source corpus.
The relationship between context incongruity and sarcasm has been studied in linguistics. We present a computational system that harnesses context incongruity as a basis for sarcasm detection. Our statistical sarcasm classifiers incorporate two kinds of incongruity features: explicit and implicit. We show the benefit of our incongruity features for two text forms tweets and discussion forum posts. Our system also outperforms two past works (with Fscore improvement of 10-20%). We also show how our features can capture intersentential incongruity.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/df23eb69a75c52f8208d572b53551581e965fae5
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In Artificial Intelligence
- Soujanya Poria
- Erik Cambria
- Navonil Majumder
- Devamanyu Hazarika
- Rada Mihalcea
- Roger Zimmermann
- Louis-philippe Morency
- Amir Zadeh
In Information Systems
- Soujanya Poria
- Devamanyu Hazarika
- Rada Mihalcea
- Ion Androutsopoulos
- Prodromos Malakasiotis
- Smaranda Muresan
- John Pavlopoulos
- Nanyun Peng
Citations in the corpus
- Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (2019, methodology): “d rule-based techniques (Veale and Hao,2010), lexical and pragmatic features (Carvalho et al.,2009), stylistic features (Davidov et al.,2010), situational disparity (Riloff et al.,2013), incongruity (Joshi et al., 2015), or user-provided annotations such as hashtags (Liebrecht et al.,2013). Resource”
- Deep Learning for User Comment Moderation (2017, methodology): “are so far in Gazzetta comments. Mihaylov and Nakov (2016) identify comments posted by opinion manipulation trolls. Dinakar et 18For sarcasm, see Davidov et al. (2010), Gonzalez-Ibanez et al. (2011), Joshi et al. (2015), Oraby et al. (2016). 19Sentiment features have been used by several methods, bu”
- A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks (2016, methodology): “While the method by (Joshi et al., 2015) obtains F1-score of 47.”
- R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge (2020, background): “…detection (Davidov et al., 2010; Gonz´alez-Ib´a˜nez et al., 2011; Riloff et al., 2013; Ghosh et al., 2015; Joshi et al., 2015b; Muresan et al., 2016; Ghosh and Veale, 2017; Ghosh et al., 2017, 2018), research on sarcasm generation is in its infancy (Joshi et al., 2015a; Mishra et al., 2019).”
- Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “s in textual systems, traditional approaches consider rule-based (Khattri et al., 2015) or statistical patterns (Gonzalez-Ib´anez et al.,˜ 2011b), stylistic patterns (Tsur et al., 2010), incongruity (Joshi et al., 2015; Tay et al., 2018a), situational disparity (Riloff et al., 2013), and hashtags (M”