Tag: intention analysis

Event and Fact Analysis

In the post “Intent on Intentions”, I’d talked a bit about the Speech Act Theory of Searle and Winograd.

In this blog post, I’d like to look at all other utterances. What purpose do utterances have if they are meaningful, but are not a Speech Act?

It turns out that meaningful utterances that do not convey Speech Acts, typically convey information. Information in turn comes in two flavours – events and facts. Facts represent states of the world (they describe relations between entities or describe properties of entities). Events represent changes.

For example, “London is in England” is a fact, whereas “London Bridge is falling down” is an event.

Entities are the things being talked about. In the sentences used to illustrate events and facts above, the following entities may be observed: “London”, “England” and “London Bridge”.

The distinction between intentions, events and facts is not watertight. There are times when utterances can cross the boundaries and fall into more than one of these categories.

Interestingly, there are different uses for the three kinds of text analysis (analysis of intention, analysis of events, and analysis of fact) and types of data that they may be applied to.

Data Sources

  • Event Analysis: News articles, because news
    reports are always about important happenings or changes in the state of the world, and
    hence are rich with events and also with facts.
  • Fact Analysis: Wikipedia, other Encyclopedias and Knowledge Bases are full of facts,
    but don’t necessarily report current events.
    They may contain information on events that
    took place in another age.
  • Intention Analysis: Emails Messages, Customer Feedback, Social
    Media Messages

Enterprise Applications

  • Event Analysis: Media Monitoring Tools,
    Opportunity Identification Tools, Conformance and Discovery Tools
  • Fact Analysis: Enterprise Search, Semantic
    Web, Logic and Inference Engines
  • Intention Analysis: CRM Tools, Collaboration Tools, Task Management Tools, Communication Devices

Here is a link to a whitepaper on the topic of doing a 360 degree analysis of text.

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Intentions and Opinions

In the last blog post, I talked about intention analysis and what it does.

Intention analysis is the identification of intentions from text. Some examples of intentions are:

a) intention to complain
b) intention to inquire
c) intention to issue a directive
d) intention to buy

In this post, I am claiming that Sentiment Analysis needs Intention Analysis.

Yes, the results of sentiment analysis will be inaccurate unless you know that the intent of the speaker is to express an opinion.

Background

When sentiment analysis was initially proposed by researchers, they applied it to the analysis of product reviews.

The intention of a reviewer is obvious. Reviewers have only one intention: the intention to opine (either to praise or to criticize).

However, with the growth of social media, especially Twitter, the same sentiment analysis methods began to be applied to the analysis of twitter streams and other social media streams.

Now that’s where there is a problem.

Not every message on Twitter that mentions a particular product or brand intends to express an opinion about the brand!

Below are a few illustrative examples.

Example 1: “Is the Canon EOS 5 a good camera?”

This sentence is not an expression of positive opinion, but an inquiry about the Canon EOS 5.

In other words, the intent of the speaker is not to express an opinion, but to inquire.

Example 2: “I am looking to buy me a good Canon camera”

Here, the intention of the user is to purchase a product (people only indicate a preference for good things … no one really looks to buy a bad camera).

However, most sentiment analysis tools will identify this sentence as an expression of positive sentiment.

Example 3: “Take me to a good movie.”

Here, the speaker’s intent is to direct someone to do something.

A directive is not an assertion, and so does not always imply an intention to opine.

Example 4: “My good old Porsche for sale (cheap)”

Here, the speaker’s intent is to talk up something they’re selling.

The intent here is not to express sentiment about the brand.

Conclusion

So, what we can learn from the above examples is that sentiment analysis is not meant to be applied without reservations to Social Media Analysis.

In other words, for sentiment analysis to be accurate when applied to social media, it needs to be supported by intention analysis.

Demonstration

We recently released a sentiment analysis API that has the ability to filter out many kinds of intention including the ones listed above. We’d love to get your thoughts on our work. The demo is available at the following URL:

Demonstration of VakSent (a Sentiment Analysis API from Aiaioo Labs)

Do write me at cohan@aiaioo.com with intent to opine!

Prior Work on Intentions

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

Definition

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.


Uses

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


Background

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.

Prior Work

Cognizant Technologies

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 [2010] 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

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

Twitchell et al have studied “Using Speech Act Theory to Model Conversations for Automated Classification and Retrieval”.

Carnegie Mellon

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

Vakintent API

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 cohan@aiaioo.com for more information.