Machine Learning for Emoji Trends

If anybody else is confused why the first usage of the heart emoji (️) in text renders as a black heart (), it&#x27;s because the page lost the 0xFE0F VARIATION SELECTOR-16 invisible character that comes after the U+2764 HEAVY BLACK HEART character to turn it into the emoji form.<p>Edit: What the, HN deleted both instances of U+2764 HEAVY BLACK HEART in the text, and left the VARIATION SELECTOR-16 behind. That&#x27;s super weird.

Pretty cool, I like how they figured out the French flag emoji is associated with italy, #montreal.<p>It reminds me of the Android app dango which does a pretty good job at suggesting emoji associated with full sentences

This seems like it took very heavy influence from <a href="http:&#x2F;&#x2F;getdango.com&#x2F;emoji-and-deep-learning&#x2F;" rel="nofollow">http:&#x2F;&#x2F;getdango.com&#x2F;emoji-and-deep-learning&#x2F;</a><p>Or actually it could be the opposite, seeing as this post is about a year old

I like this app that applies deep learning to Emojis: <a href="http:&#x2F;&#x2F;getdango.com&#x2F;emoji-and-deep-learning&#x2F;" rel="nofollow">http:&#x2F;&#x2F;getdango.com&#x2F;emoji-and-deep-learning&#x2F;</a>

This was posted in 2015, but now that fasttext (<a href="https:&#x2F;&#x2F;github.com&#x2F;facebookresearch&#x2F;fastText" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;facebookresearch&#x2F;fastText</a>), just released by Facebook and can scale to Instagram-sized datasets, can create word vectors better than word2vec which account for context (<a href="https:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;1607.04606v1.pdf" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;1607.04606v1.pdf</a>), this type of analysis will only improve in the future.

I imagine that a natural language AI which uses emojis would be better at passing a Turing test