I'm looking for a something that can detect and characterize the tone of a written text the way we do naturally when speaking. For example detecting sarcasm in a tweet.
The general category of research is sentiment analysis:<p><a href="https://en.wikipedia.org/wiki/Sentiment_analysis" rel="nofollow">https://en.wikipedia.org/wiki/Sentiment_analysis</a><p>However that's as much as I know. If you get lucky maybe an expert or user of these tools will see your post!
(2000) <a href="https://www.qualcomm.com/news/releases/2000/09/11/qualcomms-eudora-50-spices-email-experience-hot-new-time-saving-tools-keep" rel="nofollow">https://www.qualcomm.com/news/releases/2000/09/11/qualcomms-...</a><p>> MoodWatch, based on rhetorical theories developed by David Kaufer, chairman of the English department of Carnegie Mellon University, employs a very fast and efficient algorithm to identify words and phrases that might be offensive. As such, it's efficient at flagging potentially offensive messages, but it is up to the user to decide if a message 'deserves the chilies.'<p>This is from a release announcement of Eudora, a bygone, once-popular e-mail client. (Kudos to Qualcomm to keeping this online 22 years later!) "Deserve the chilies" refers to how the client would display one to three chili pepper icons if it felt your e-mail was rude or offensive. (Software used to be whimsical.)<p>There was a white paper that described how they trained the feature on postings from the Usenet group alt.flame. Also, the source code to Eudora is now available from the Computer History Museum, including MoodWatch: <a href="https://computerhistory.org/blog/the-eudora-email-client-source-code/" rel="nofollow">https://computerhistory.org/blog/the-eudora-email-client-sou...</a>
You're looking for sentiment analysis and you won't get good results unless the text is in a specific domain. I did this work for a previous company where we analyzed restaurant reviews to extract actionable feedback. The trouble is that the written word is missing most of the nonverbal cues that give you the tone for spoken word (think air quotes or rolled eyes). So when you see a review that says "My steak came out well done...great job guys!" It looks positive but could be sarcastic. Or when someone says "We came to Taco Bell because we were looking for the most authentic Mexican cuisine" of course this is sarcastic but there is no way you could teach a computer to spot that.
If you think about this you'll realize that it is impossible to detect tone from text because it's not possible to convey tone with text alone. Tone is auditory, text is visual, and there is no way to recover one from the other. For an obvious demonstration of this watch a movie on mute and then listen to the audio of the movie without the accompanying visuals.
IBM used to have one but it was deprecated this year. Looks like there’s a follow up product with a decent free tier. <a href="https://cloud.ibm.com/apidocs/tone-analyzer" rel="nofollow">https://cloud.ibm.com/apidocs/tone-analyzer</a>
See this tutorial: <a href="https://www.youtube.com/watch?v=ZUqB-luawZg" rel="nofollow">https://www.youtube.com/watch?v=ZUqB-luawZg</a> It kind of works but this is a very very difficult problem to solve.