A demo project to play word embedding and emoji with twitter data. Let's see how we can make a smarter emoji predictor.

Believe it or not, we have 1800+ emoji from standard Unicode. How to find one from them? Traditionally, we search key words of emoji description. We will use word embedding to find the best match with a context. The results reflects real users habit from social media. Now you are guided with most knowledgeable emoji master :)

Check the site http://nlpfun.com/emoji for a preview of what we can do next with the model!

The zip file in the data folder is a 1M sentences with emoji from Twitter about 2017-Jan. It is randomly selected set from a much bigger corpus. Unzip the corpus.txt directly into the data folder for training.

Run train.py file. Don't forget to set correct parameters like vector size, windows size, etc. It will dump a model and a raw text file for the embedding.

Install the gensim first with python 3.5

pip install -r requirements.txt

Let's run the model.py and see the console output. If you ever played word2vec before, you know the answer for the famous play 'King' - 'man' + 'woman' = ? . Let check the results with our simple twitter data.

query + ['king', 'woman'] - ['man']
[('queen', 0.73), ('crown', 0.7), ('goddess', 0.7), ('princess', 0.7), ('actress', 0.69)]

query + ['china', 'tokyo'] - ['beijing']
[('japan', 0.73), ('theatre', 0.69), ('europe', 0.68), ('nyc', 0.67), ('nuttiness', 0.67)]

query + ['dog', 'cats'] - ['dogs']
[('cat', 0.92), ('kitten', 0.75), ('puppy', 0.73), ('coworker', 0.71), ('mom', 0.69)]

Finally, let's see how well we can find emoji by key words!

query + ['cat'] - []
[('🐱', 0.66), ('🐶', 0.5), ('🐈', 0.47), ('🐰', 0.42), ('🐕', 0.4), ('🐭', 0.39)]

query + ['happy', 'new', 'year'] - []
[('🎉', 0.56), ('🎄', 0.38), ('🏆', 0.38), ('🍾', 0.37), ('🎁', 0.36)]

query + ['king', 'woman'] - ['man']
[('👑', 0.63), ('👸', 0.59), ('🦁', 0.56)]

query + ['💗'] - []
[('💓', 0.94), ('💜', 0.92), ('💖', 0.92), ('💘', 0.91), ('💞', 0.91), ('💕', 0.91), ('💛', 0.81), ('💙', 0.81), ('💟', 0.79), ('💝', 0.72)]

To better understanding what's going on inside, here are some recommendations:
https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/ http://web.stanford.edu/class/cs224n/assignment1/index.html