Emojis represent an essential means of expressing sentiments such as opinions and attitudes in computer-mediated communication, especially in chats and social media. To effectively capture these sentiments, the sentiments associated with the emojis used must be known. Previous approaches to determining the sentiments expressed with emojis require a large amount of manual annotation. For many emojis, especially less frequently used platform-specific emojis, studies on expressed sentiments do not yet exist. Therefore, these emojis cannot be considered in sentiment analyses so far. In this work, a method for effective and efficient determination of emojis’ sentiments and their compilation in a sentiment lexicon was developed. The determined sentiments are compiled as a sentiment lexicon. For this purpose, software was created in Python to process collections of texts into a corpus. The software derives the emojis’ sentiments as valence values based on the sentiments of the texts in which the emojis appear. The lexicons produced by the method can be used in lexicon-based sentiment analysis approaches. The method also derives other information on the emojis and their usage that can be used to assess the sentiment lexicon produced and the usage of the emojis. Using the developed method, two analyses were conducted with corpora of different text sources. The results and subsequent comparisons with existing sentiment lexicons have shown that the developed method is able to efficiently produce similar results as sentiment lexicons produced with manual annotation.