Nocode functions 🔎
Try it, just click on the link:
Just replace "en" by "fr" in the url if your text is in French. You feedback is welcome!
... or read below about what it does, and how it works:
The function is programmed in Java. His code is accessible freely on Github. Its performance is excellent because it does not rely on part-of-speech taggging (POS tagging) a>, which makes it fast. Concurrent computing techniques are used in sub-functions and great attention has been paid to overall performance.
The function examines each term of the text and applies a series of rules to determine the effect on the sentiment. It also systematically considers emojis, punctuation, hashtags, variations in spelling, capitalized words... to determine the sentiment.
The principles followed by the tool are described in this academic publication about Umigon, published in the anthology of the Association fo Computational Linguistics. The tool follows these steps:
yeeeahhh!
yeah!
Some tools provide a scoring to express the strength of the sentiment: a large value for a very positive sentiment, and a very low value for a negative sentiment. Zero represents a neutral sentiment. In my experience, these scorings are not super reliable, except for the obvious cases. "Horrible" will score really low, and "wonderful" will score really high. But in the middle, things are less straightforward and the scorings are much harder to interpret - I would not advise to rely on them. A more promising road would be to introduce emotions to tease out the finer nunances of sentiment, beyond the positive / neutral / negative categories. Drop an email at admin@clementlevallois.net if you are interested in this direction of research.
This tool identifies subjective markers of sentiment, NOT positive or negative factual statements. To give an example:
This country is at war
War is horrible :(
War of the sexes is an exciting research topic!