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In their new inventions, a team of IIT B claim that they can automatically detect if a person is drunk by reading their text messages.
“This is a first-of-its-kind work that provides quantitative evidence that a text-based analysis may be useful for drunk-texting prediction,” said Aditya Joshi, PhD student from IITB-Monash Research Academy to media.
Joshi has worked on this project along with supervisors, Professor Pushpak Bhattacharyya from IIT-Bombay, and Professor Mark J Carman from Monash University, Melbourne.
The idea to invent such program was sparked in Joshi’s head when he received a message from his friend. “We use a statistical classifier to predict whether a tweet is written by a user under the influence of alcohol. The current testing is on tweets, while it would typically apply to any user-generated text on social media,” said Joshi.
The team has used two sets of features, ‘N-gram’ based features, and ‘stylistic’ features that qualify typical styles of writing that drunk texts may have, such as high sentiment-bearing words, capitalisation and spelling mistakes, among others.
According to the team, the project will be useful for a mental health professional or a relative trying to monitor a person’s behavior and those trying to avoid private information at workplace being leaked through their emails or texts. The team members further said that it would be useful in drunk driving cases too. We made interesting observations: it includes spelling mistakes, use of drunk-related words (like alcohol, tonight, drunk, etc) and capitalisation tend to be the topmost discriminating feature.
The algorithm of the program is automatic, and hence, faster and more cost-effective, could predict in 64 per cent of the tweets if it was written under the influence of alcohol.
