The data was obtained through the utilization of snscrape. The query used for retrieval was based on individual emojis. Relevant data was identified, and subsequently assessed for the presence of emojis as well as the sentence's adherence to English language conventions. The language detection analysis was conducted using pycld3, which was inspired by the paper "The WiLI benchmark dataset for written language identification." Each csv file consists of 20,000 distinct data entries. The file name is created based on emoji package (emoji.EMOJI_DATA) in Python.

It should be noted that given the possible occurrence of small errors associated with pycld3, along with the potential for multiple emojis per data entry, there may exist instances of non-English tweets or duplicated tweets across different CSV files.