423 research outputs found
"With 1 follower I must be AWESOME :P". Exploring the role of irony markers in irony recognition
Conversations in social media often contain the use of irony or sarcasm, when
the users say the opposite of what they really mean. Irony markers are the
meta-communicative clues that inform the reader that an utterance is ironic. We
propose a thorough analysis of theoretically grounded irony markers in two
social media platforms: and . Classification and frequency
analysis show that for , typographic markers such as emoticons and
emojis are the most discriminative markers to recognize ironic utterances,
while for the morphological markers (e.g., interjections, tag
questions) are the most discriminative.Comment: ICWSM 201
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Sarcasm detection on Twitter
State-of-the-art approaches for sarcasm detection in social media combine lexical clues with contextual information surrounding the potentially sarcastic posting including author information. This article presents detailed methods for performing contextualizing sarcasm detection on Twitter, including data extraction, feature engineering and classification model settings. I reproduce the state-of-the-art results reported by Bamman and Smith (2015).Informatio
Detecting Sarcasm in Multimodal Social Platforms
Sarcasm is a peculiar form of sentiment expression, where the surface
sentiment differs from the implied sentiment. The detection of sarcasm in
social media platforms has been applied in the past mainly to textual
utterances where lexical indicators (such as interjections and intensifiers),
linguistic markers, and contextual information (such as user profiles, or past
conversations) were used to detect the sarcastic tone. However, modern social
media platforms allow to create multimodal messages where audiovisual content
is integrated with the text, making the analysis of a mode in isolation
partial. In our work, we first study the relationship between the textual and
visual aspects in multimodal posts from three major social media platforms,
i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to
quantify the extent to which images are perceived as necessary by human
annotators. Moreover, we propose two different computational frameworks to
detect sarcasm that integrate the textual and visual modalities. The first
approach exploits visual semantics trained on an external dataset, and
concatenates the semantics features with state-of-the-art textual features. The
second method adapts a visual neural network initialized with parameters
trained on ImageNet to multimodal sarcastic posts. Results show the positive
effect of combining modalities for the detection of sarcasm across platforms
and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of
ACM Multimedia 201
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