Journal ArticleDOI
Sarcasm Detection for Workplace Stress Management
TLDR
The currently developed system helps to detect the sarcastic emotions by applying different methodologies on several types of statements to help corporations and other big organizations to identify reasons behind sarcastic behavior or increased anxiety.Abstract:
Working stress is becoming very common. Handling working stress at the workplace is really going to be challenging. As a result, most of the time most of the time people start behaving in sarcastic ways through verbal communication, through different gestures, using emoticons, or writing reviews or comments that leads to increasing their anxiety sometimes promotes depression. It is difficult to identify sarcasm in written notes or communication. Feedback analysis is not a direct method since feedback or employer reviews are written in more formal language. This motivates the authors to work on the employee feedback system. The currently developed system helps to detect the sarcastic emotions by applying different methodologies on several types of statements. This will help corporations and other big organizations to identify reasons behind sarcastic behavior or increased anxiety. As a result, they can promote counseling programs, psychological treatment, or yoga-meditation camps.read more
Citations
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Journal ArticleDOI
Hybrid attention-based Long Short-Term Memory network for sarcasm identification
TL;DR: A hybrid attention-based Long Short Term Memory (HA-LSTM) network to identify sarcastic statements is proposed, which combines 16 different linguistic features in their hidden layers and shows an improvement in the performance of the model in comparison with other state-of-the-art models.
References
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Proceedings Article
Identifying Sarcasm in Twitter: A Closer Look
TL;DR: This work reports on a method for constructing a corpus of sarcastic Twitter messages in which determination of the sarcasm of each message has been made by its author and uses this reliable corpus to compare sarcastic utterances in Twitter to utterances that express positive or negative attitudes without sarcasm.
Proceedings Article
Sarcasm as Contrast between a Positive Sentiment and Negative Situation
Ellen Riloff,Ashequl Qadir,Prafullakumar Surve,Lalindra De Silva,Nathan Gilbert,Ruihong Huang +5 more
TL;DR: This work develops a sarcasm recognizer that automatically learns lists of positive sentiment phrases and negative situation phrases from sarcastic tweets and shows that identifying contrasting contexts using the phrases learned through bootstrapping yields improved recall for sarcasm recognition.
Proceedings Article
Semi-supervised recognition of sarcastic sentences in Twitter and Amazon
TL;DR: This paper experiments with semi-supervised sarcasm identification on two very different data sets: a collection of 5.9 million tweets collected from Twitter, and aCollection of 66000 product reviews from Amazon.
Proceedings ArticleDOI
Sarcasm Detection on Twitter: A Behavioral Modeling Approach
TL;DR: This paper employs theories from behavioral and psychological studies to construct a behavioral modeling framework tuned for detecting sarcasm on Twitter and identifies behavioral traits intrinsic to users expressing sarcasm using the user's past tweets.
Proceedings Article
Contextualized Sarcasm Detection on Twitter
David Bamman,Noah A. Smith +1 more
TL;DR: By including extra-linguistic information from the context of an utterance on Twitter — such as properties of the author, the audience and the immediate communicative environment — this work is able to achieve gains in accuracy compared to purely linguistic features in the detection of this complex phenomenon, while also shedding light on features of interpersonal interaction that enable sarcasm in conversation.