Emojis have been frequently used to express users’ sentiments, emotions, and feelings in text-based communication. We presented a simple and efficient method for automatically constructing an emoji sentiment lexicon with arbitrary sentiment categories. The proposed method extracts sentiment words from WordNet-Affect and calculates the co-occurrence frequency between the sentiment words and each emoji. Based on the ratio of the number of occurrences of each emoji among the sentiment categories, each emoji is assigned a multidimensional vector whose elements indicate the strength of the corresponding sentiment. In experiments conducted on a collection of tweets, we showed a high correlation between the conventional lexicon and our lexicon for three sentiment categories. We also showed the results for a new lexicon constructed with additional sentiment categories.
If you use our lexicons, please refer to the following paper.
M. Kimura and M. Katsurai, “Automatic Construction of an Emoji Sentiment Lexicon,” Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 1033–1036, 2017.