We have been exploring intention analysis for some time now and we are pleased to announce the launch of the first ever commercial API for broad-based intention analysis, called Vakintent.
Here is a demo of the Vakintent Intention Analysis API: Demonstration of VakIntent, the Intention Analysis API from Aiaioo Labs
Intention Analysis is the identification of intentions from text, be it the intention to purchase or the intention to sell or to complain, accuse or to inquire, in incoming customer messages or in call center transcripts.
Intention Analysis has already given us some evidence of its usefulness.
In July 2011, we used intention analysis to study the GooglePlus launch. We especially looked at quit intentions to see how frequently people were threatening to quit FB over time and saw how the number dropped sharply once people got to try GooglePlus (once the by-invite-only period ended).
This was a powerful observation, because in just four days, we could tell that GooglePlus couldn’t replace Facebook, at least not yet. Here is the study: http://www.aiaioo.com/cami
The work that intention analysis is based on goes as far back as 1962 when J. L. Austin noted that not all utterances are statements whose truth and falsity are at stake, and that there was a class of utterances like “I pronounce you husband and wife” that are actions [taken from Winograd, 1987].
(I recently found the Winograd paper on his website: http://hci.stanford.edu/winograd/papers/language-action.html)
In 1975, Searle identified the following broad categories of illocutionary (causing an action to happen) speech acts [from Winograd, 1987]:
- Assertive – Committing the speaker to the truth of a proposition
- Directive – Attempting to get the listener to do something
- Commissive – Committing the speaker to a course of action
- Declaration – Bringing about something (eg., pronouncing someone married)
- Expressive – Expressing a psychological state
Interestingly, the expressives include expression of opinion which corresponds to the modern day task of sentiment analysis.
There was a paper at ACL 2010 titled “Wishful Thinking – Finding suggestions and ‘buy’ wishes from product reviews” http://aclweb.org/anthology/W/W10/W10-0207.pdf by Krishna Bhavsar et al from Cognizant Technologies .
Lampert and Dale
Another recent attempt to build computer systems capable of analysing intention was made by Robert Dale and Andrew Lampert at Macquarie University. A paper that I’d recommend to you is their work on detecting emails containing requests for action: “Andrew Lampert, Robert Dale and Cécile Paris  Detecting Emails Containing Requests for Action. Pages 984–992 in Proceedings of NAACL 2010, 1st–6th June 2010, Los Angeles, USA“. Our own work leads us to believe that the difficulty of detecting directives is rather higher than for other intentions, so what they’ve done in this project is quite impressive.
WisdomTap (www.wisdomtap.com) has a very interesting buy intention offering. Their value proposition is “Your Customers announce their intent to buy by asking for product and service recommendations on Twitter. We find customers who need your products and services. We connect you to your customers at the right time.”
Twitchell et al have studied “Using Speech Act Theory to Model Conversations for Automated Classification and Retrieval”.
CMU has released a speech act corpus: through the Jangada and Ciranda projects.
Vakintent Demonstration Consoles
Here are some links to demos:
Name Description URL
Vakintent Intention Demo Demonstration of VakIntent, the Intention Analysis API from Aiaioo Labs
Vaksent Sentiment Dem Demonstration of VakSent, the Sentiment Analysis API from Aiaioo Labs
Case Study URL
Competitive Analysis http://www.aiaioo.com/cami
The Vakintent API offered by Aiaioo Labs can identify 11 intentions, the objects of those intentions and their holders.
Please feel free to write me at email@example.com for more information.