iSarcasm: A Dataset of Intended Sarcasm

Oprea, Silviu; Magdy, Walid

doi:10.18653/v1/2020.acl-main.118

Cited by 37 publications

(58 citation statements)

References 34 publications

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“…Recently, Oprea & Magdy (2019) proposed the iSarcasm dataset, which annotates labels by the original writers for the sarcastic posts. This kind of annotation is promising as it circumvents the issues mentioned above while capturing the intended sarcasm.…”

Section: Annotation Challenges: Intended Vs Perceived Sarcasmmentioning

confidence: 99%

Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Poria¹,

Hazarika²,

Majumder³

et al. 2020

Preprint

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Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks -such as sentiment polarity classification -and datasets, there is an underlying perception that this field has reached its maturity. In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field that are necessary to attain true sentiment understanding. We analyze the significant leaps responsible for its current relevance. Further, we attempt to chart a possible course for this field that covers many overlooked and unanswered questions.

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Section: Annotation Challenges: Intended Vs Perceived Sarcasmmentioning

confidence: 99%

Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Poria¹,

Hazarika²,

Majumder³

et al. 2020

Preprint

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show abstract

“…They also did not deal with the other SA challenges. ISarcasm dataset [27] was introduced to solve the limitation of other datasets for the sarcasm detection task. They tried to solve the sarcasm problem in SA without regard to the different challenges of SA.…”

Section: Related Workmentioning

confidence: 99%

“…They tried to solve the sarcasm problem in SA without regard to the different challenges of SA. All the works [22,23,24,25,26,27] tried to solve sarcasm problems without the other challenges.…”

Section: Related Workmentioning

confidence: 99%

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Key Challenges and Proposed Solutions to Design Sentiment Analysis System

2022

IJIES

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Sentiment analysis (SA) is one of the most important tasks in the natural language processing (NLP) field. Many researchers have been trying to build an efficient SA system for many applications such as detection of terrorist activities, customer support management, analyzing customer feedback, market research, competitive research, and many others. Almost all these researches deal with a classification task, and they tried to solve one or two of the challenges that face this task. In this research, a complete SA system was constructed for processing five main challenges in SA and studying the effect of each one on the system. These challenges are the processing of negation, multi-polarity of words, text multi-polarity, semantically ambiguous words (exact meaning), and sarcasm. Some solutions were introduced for these challenges for getting high accuracy. It is the first work for studying the effect of these five challenges collectively with novel solutions to some of them. For aspect-based SA, three different classifiers were used; support vector machine (SVM), maximum entropy (MaxEnt), and long short-term memory (LSTM). The best results for f-measure were 0.833, 0.852, and 0.875 using the LSTM method on Foursquare ABSA, SemEval2014 (laptop and restaurant) datasets, respectively. We induced the most effective negation processing on the SA system from many test scenarios, followed by multi-polarity of words, semantically ambiguous words, text multi-polarity, and sarcasm, respectively. Also, the proposed sarcasm processing technique was evaluated on two annotated corpora with sarcasm, iSarcasm, and Rillof datasets, before applying it on SA.

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“…We collected a total of 30 responses for each background, making a total of 240 tweets for both backgrounds, with a proportion of 1:4 of sarcastic to non-sarcastic tweets. This dataset is a subset of what constitutes the iSarcasm dataset [40].…”

Section: Us Malementioning

confidence: 99%

The Effect of Sociocultural Variables on Sarcasm Communication Online

Oprea

Magdy

2020

Proc. ACM Hum.-Comput. Interact.

Self Cite

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Online social networks (OSN) play an essential role for connecting people and allowing them to communicate online. OSN users share their thoughts, moments, and news with their network. The messages they share online can include sarcastic posts, where the intended meaning expressed by the written text is different from the literal one. This could result in miscommunication. Previous research in psycholinguistics has studied the sociocultural factors the might lead to sarcasm misunderstanding between speakers and listeners. However, there is a lack of such studies in the context of OSN. In this paper we fill this gap by performing a quantitative analysis on the influence of sociocultural variables, including gender, age, country, and English language nativeness, on the effectiveness of sarcastic communication online. We collect examples of sarcastic tweets directly from the authors who posted them. Further, we ask third-party annotators of different sociocultural backgrounds to label these tweets for sarcasm. Our analysis indicates that age, English language nativeness, and country are significantly influential and should be considered in the design of future social analysis tools that either study sarcasm directly, or look at related phenomena where sarcasm may have an influence. We also make observations about the social ecology surrounding sarcastic exchanges on OSNs. We conclude by suggesting ways in which our findings can be included in future work.

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iSarcasm: A Dataset of Intended Sarcasm

Cited by 37 publications

References 34 publications

Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Key Challenges and Proposed Solutions to Design Sentiment Analysis System

The Effect of Sociocultural Variables on Sarcasm Communication Online

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