  In the absence of nonverbal cues during messaging communication, users
express part of their emotions using emojis. Thus, having emojis in the
vocabulary of text messaging language models can significantly improve many
natural language processing (NLP) applications such as online communication
analysis. On the other hand, word embedding models are usually trained on a
very large corpus of text such as Wikipedia or Google News datasets that
include very few samples with emojis. In this study, we create emojiSpace,
which is a combined word-emoji embedding using the word2vec model from the
Genism library in Python. We trained emojiSpace on a corpus of more than 4
billion tweets and evaluated it by implementing sentiment analysis on a Twitter
dataset containing more than 67 million tweets as an extrinsic task. For this
task, we compared the performance of two different classifiers of random forest
(RF) and linear support vector machine (SVM). For evaluation, we compared
emojiSpace performance with two other pre-trained embeddings and demonstrated
that emojiSpace outperforms both.
