Emot is a Python library designed for high-performance detection and extraction of emojis and emoticons from text, particularly useful for large-scale datasets. It utilizes advanced dynamic pattern generation based on an internal database. The current version, 3.1, released in August 2021, focuses on performance and bulk processing capabilities. The library provides details like the value, meaning, location, and presence (flag) of detected symbols.
pip install emot --upgradeVerified import paths — ran on the pinned version, not inferred.
Initialize the `emot` object and then use its `emoji()` or `emoticons()` methods to detect and extract symbols from a given string. The methods return a dictionary containing lists for 'value', 'location', 'mean', and a 'flag' indicating presence.
Update your code to expect a dictionary output and access keys like `result['value']` instead of iterating over a list of dictionaries.
Change `import emot` to `emot_obj = emot.core.emot()` and then call methods using `emot_obj.emoji(text)`. Ensure your environment uses Python 3.x.
Implement post-processing logic to filter out skin tone modifiers if they are not desired as individual detections. For example, check if the 'mean' field contains ':_skin_tone:' and remove such entries.
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