talkAItive’s story starts in 2017 when a group of data professionals came together to build a vision where people and institutions are able to understand the big picture and connect the dots to build deeper relationships through emotional contexts.
Our research showed a clear opportunity to learn the emotional contexts, which differed from the social engagement metrics offered by the leading social listening companies.
We quickly ran into the problem of detecting sarcasm and language usage-based contexts which required a different approach from the word dictionary-based analysis.
We found that usage makes all the difference. E.g. “Killer app …” VS “Killer dog …” communicate contrasting emotional intensity but Killer in isolation can only be negative.
In 2018, the team achieved our first research success through detection of sarcasm in Twitter chatter which was peer-reviewed by the academic community.
This gave us the early foundation and path to develop novel methods which learn the usage of language by context.
Identifying these contexts related to place, person, product or brand and then measuring the sentimental and emotional intensity across time is our focus of research and product development.
"A method and system for dynamically generating a sentiment community based on varied on-line content sources using a linguistic sentiment engine A novel approach accounts for a sarcasm sentiment by detecting same and accounting for its influence information of the transient sentiment community Applicable to identifying and capturing sentiment communities such as for product reviews, service reviews, political and non-political commentary, and other content".
Our patent has given us the focus and the future path to become the world’s best emotional understanding technology company.
We extended talkAItive’s abilities to detect language which points towards the same “Emotional Context” but has been uttered using a variety of phrases.
This helps our clients quickly summarize the strength of trend and track narratives across time periods. Clients can even tell talkAItive to track specific narrative and monitor narrative that sticks vs fizzles out.
This patent was awarded in February 2021 and we have extended it further to include the novel ways talkAItive detects and monitors emotional intensity across written/spoken word and soon in images.
The journey so far would not have been possible without a team that actually cares about this vision and are personally motivated to make it a commercial success.
Our learning as a group has been phenomenal and discussions cover everything from algorithm design, deep learning networks, NLP, user experience, and application of our insights.
We are constantly trying to add ways to learn emotional intensity in digital media. If you have a related idea across any of those disciplines, please drop us a line at email@example.com!
Head, Machine Learning