11,112 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
Don't Let Me Be Misunderstood: Comparing Intentions and Perceptions in Online Discussions
Discourse involves two perspectives: a person's intention in making an
utterance and others' perception of that utterance. The misalignment between
these perspectives can lead to undesirable outcomes, such as misunderstandings,
low productivity and even overt strife. In this work, we present a
computational framework for exploring and comparing both perspectives in online
public discussions.
We combine logged data about public comments on Facebook with a survey of
over 16,000 people about their intentions in writing these comments or about
their perceptions of comments that others had written. Unlike previous studies
of online discussions that have largely relied on third-party labels to
quantify properties such as sentiment and subjectivity, our approach also
directly captures what the speakers actually intended when writing their
comments. In particular, our analysis focuses on judgments of whether a comment
is stating a fact or an opinion, since these concepts were shown to be often
confused.
We show that intentions and perceptions diverge in consequential ways. People
are more likely to perceive opinions than to intend them, and linguistic cues
that signal how an utterance is intended can differ from those that signal how
it will be perceived. Further, this misalignment between intentions and
perceptions can be linked to the future health of a conversation: when a
comment whose author intended to share a fact is misperceived as sharing an
opinion, the subsequent conversation is more likely to derail into uncivil
behavior than when the comment is perceived as intended. Altogether, these
findings may inform the design of discussion platforms that better promote
positive interactions.Comment: Proceedings of The Web Conference (WWW) 202
How did the discussion go: Discourse act classification in social media conversations
We propose a novel attention based hierarchical LSTM model to classify
discourse act sequences in social media conversations, aimed at mining data
from online discussion using textual meanings beyond sentence level. The very
uniqueness of the task is the complete categorization of possible pragmatic
roles in informal textual discussions, contrary to extraction of
question-answers, stance detection or sarcasm identification which are very
much role specific tasks. Early attempt was made on a Reddit discussion
dataset. We train our model on the same data, and present test results on two
different datasets, one from Reddit and one from Facebook. Our proposed model
outperformed the previous one in terms of domain independence; without using
platform-dependent structural features, our hierarchical LSTM with word
relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively
to predict discourse roles of comments in Reddit and Facebook discussions.
Efficiency of recurrent and convolutional architectures in order to learn
discursive representation on the same task has been presented and analyzed,
with different word and comment embedding schemes. Our attention mechanism
enables us to inquire into relevance ordering of text segments according to
their roles in discourse. We present a human annotator experiment to unveil
important observations about modeling and data annotation. Equipped with our
text-based discourse identification model, we inquire into how heterogeneous
non-textual features like location, time, leaning of information etc. play
their roles in charaterizing online discussions on Facebook
Annotating Errors and Emotions in Human-Chatbot Interactions in Italian
This paper describes a novel annotation scheme specifically designed for a customer-service context where written interactions take place between a given user and the chatbot of an Italian telecommunication company. More specifically, the scheme aims to detect and highlight two aspects: the presence of errors in the conversation on both sides (i.e. customer and chatbot) and the “emotional load” of the conversation. This can be inferred from the presence of emotions of some kind (especially negative ones) in the customer messages, and from the possible empathic
responses provided by the agent. The dataset annotated according to this scheme is currently used to develop the prototype of a rule-based Natural Language Generation system aimed at improving the chatbot responses and the customer experience overall
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