544 research outputs found
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
A Multimodal Approach to Sarcasm Detection on Social Media
In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly more difficult than categorizing sentiment. Because, in sarcasm, the intended meaning of the expression by the user is opposite to the literal meaning. Because of its complex nature, it is often difficult even for human to detect sarcasm without proper context. However, people on social media succeed in detecting sarcasm despite interacting with strangers across the world. That motivates us to investigate the human process of detecting sarcasm on social media where abundant context information is often unavailable and the group of users communicating with each other are rarely well-acquainted. We have conducted a qualitative study to examine the patterns of users conveying sarcasm on social media. Whereas most sarcasm detection systems deal in word-by-word basis to accomplish their goal, we focused on the holistic sentiment conveyed by the post. We argue that utilization of word-level information will limit the systems performance to the domain of the dataset used to train the system and might not perform well for non-English language. As an endeavor to make our system less dependent on text data, we proposed a multimodal approach for sarcasm detection. We showed the applicability of images and reaction emoticons as other sources of hints about the sentiment of the post. Our research showed the superior results from a multimodal approach when compared to a unimodal approach. Multimodal sarcasm detection systems, as the one presented in this research, with the inclusion of more modes or sources of data might lead to a better sarcasm detection model
Deriving Verb Predicates By Clustering Verbs with Arguments
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
have proved useful, but have limited coverage. Verb classes automatically
induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
hand, can give clusters with much larger coverage, and can be adapted to
specific corpora such as Twitter. We present a method for clustering the
outputs of VerbKB: verbs with their multiple argument types, e.g.
"marry(person, person)", "feel(person, emotion)." We make use of a novel
low-dimensional embedding of verbs and their arguments to produce high quality
clusters in which the same verb can be in different clusters depending on its
argument type. The resulting verb clusters do a better job than hand-built
clusters of predicting sarcasm, sentiment, and locus of control in tweets
Computational Sarcasm Analysis on Social Media: A Systematic Review
Sarcasm can be defined as saying or writing the opposite of what one truly
wants to express, usually to insult, irritate, or amuse someone. Because of the
obscure nature of sarcasm in textual data, detecting it is difficult and of
great interest to the sentiment analysis research community. Though the
research in sarcasm detection spans more than a decade, some significant
advancements have been made recently, including employing unsupervised
pre-trained transformers in multimodal environments and integrating context to
identify sarcasm. In this study, we aim to provide a brief overview of recent
advancements and trends in computational sarcasm research for the English
language. We describe relevant datasets, methodologies, trends, issues,
challenges, and tasks relating to sarcasm that are beyond detection. Our study
provides well-summarized tables of sarcasm datasets, sarcastic features and
their extraction methods, and performance analysis of various approaches which
can help researchers in related domains understand current state-of-the-art
practices in sarcasm detection.Comment: 50 pages, 3 tables, Submitted to 'Data Mining and Knowledge
Discovery' for possible publicatio
BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
The dramatic increase in the use of social media platforms for information
sharing has also fueled a steep growth in online abuse. A simple yet effective
way of abusing individuals or communities is by creating memes, which often
integrate an image with a short piece of text layered on top of it. Such
harmful elements are in rampant use and are a threat to online safety. Hence it
is necessary to develop efficient models to detect and flag abusive memes. The
problem becomes more challenging in a low-resource setting (e.g., Bengali
memes, i.e., images with Bengali text embedded on it) because of the absence of
benchmark datasets on which AI models could be trained. In this paper we bridge
this gap by building a Bengali meme dataset. To setup an effective benchmark we
implement several baseline models for classifying abusive memes using this
dataset. We observe that multimodal models that use both textual and visual
information outperform unimodal models. Our best-performing model achieves a
macro F1 score of 70.51. Finally, we perform a qualitative error analysis of
the misclassified memes of the best-performing text-based, image-based and
multimodal models.Comment: EMNLP 2023 (main conference
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