  Most social media platforms are largely based on text, and users often write
posts to describe where they are, what they are seeing, and how they are
feeling. Because written text lacks the emotional cues of spoken and
face-to-face dialogue, ambiguities are common in written language. This problem
is exacerbated in the short, informal nature of many social media posts. To
bypass this issue, a suite of special characters called "emojis," which are
small pictograms, are embedded within the text. Many emojis are small
depictions of facial expressions designed to help disambiguate the emotional
meaning of the text. However, a new ambiguity arises in the way that emojis are
rendered. Every platform (Windows, Mac, and Android, to name a few) renders
emojis according to their own style. In fact, it has been shown that some
emojis can be rendered so differently that they look "happy" on some platforms,
and "sad" on others. In this work, we use real-world data to verify the
existence of this problem. We verify that the usage of the same emoji can be
significantly different across platforms, with some emojis exhibiting different
sentiment polarities on different platforms. We propose a solution to identify
the intended emoji based on the platform-specific nature of the emoji used by
the author of a social media post. We apply our solution to sentiment analysis,
a task that can benefit from the emoji calibration technique we use in this
work. We conduct experiments to evaluate the effectiveness of the mapping in
this task.
