  Online media, such as blogs and social networking sites, generate massive
volumes of unstructured data of great interest to analyze the opinions and
sentiments of individuals and organizations. Novel approaches beyond Natural
Language Processing are necessary to quantify these opinions with polarity
metrics. So far, the sentiment expressed by emojis has received little
attention. The use of symbols, however, has boomed in the past four years.
About twenty billion are typed in Twitter nowadays, and new emojis keep
appearing in each new Unicode version, making them increasingly relevant to
sentiment analysis tasks. This has motivated us to propose a novel approach to
predict the sentiments expressed by emojis in online textual messages, such as
tweets, that does not require human effort to manually annotate data and saves
valuable time for other analysis tasks. For this purpose, we automatically
constructed a novel emoji sentiment lexicon using an unsupervised sentiment
analysis system based on the definitions given by emoji creators in Emojipedia.
Additionally, we automatically created lexicon variants by also considering the
sentiment distribution of the informal texts accompanying emojis. All these
lexica are evaluated and compared regarding the improvement obtained by
including them in sentiment analysis of the annotated datasets provided by
Kralj Novak et al. (2015). The results confirm the competitiveness of our
approach.
