Wishful Thinking and Leprechauns

I recently came across a lovely cartoon on Leprechauns and social media.

Fortunately for us, we have a leprechaun in the office.

(So, now you know where we get our startup funding from).

Here’s a picture of the guy (that’s the cubicle he shares with Selasdia):

Just kidding!

One of our business partners brought the little pewter leprechaun in the picture back to India for us from Ireland.

It might have once been popularly believed in Ireland that leprechauns had the ability to grant Wishes.

And we find Wishes immensely interesting because some of the earliest work on Intention Analysis started out as an attempt to detect and classify Wishes.

In fact, one of the loveliest papers on the subject started out with an attempt to study what people wished for (wanted) on New Years Day.

You can read the paper here:  http://pages.cs.wisc.edu/~jerryzhu/pub/wish.pdf

It has a very beautiful title: “May All Your Wishes Come True:  A Study of Wishes and How to Recognize Them”

You also find the word Wishes in the title of one of the first attempts in research literature to find “buy” intentions:

http://www.aclweb.org/anthology-new/W/W10/W10-0207.pdf

It is a paper titled, again quite poetically (what’s with Wishes and beautiful titles!) “Wishful Thinking – Finding suggestions and ‘buy’ wishes from product reviews”.

This paper was written by a research team working at Cognizant (India) in 2010.

Sentiment Analysis, Intention Analysis and the Direction of Fit

As you know, for some years now, all of us who form part of the NLP research team at Aiaioo Labs have been working on a technology for text analysis called ‘Intention Analysis‘.

It was something few had heard of when we started.

Today, a lot more people know the term.

But there has been not a great deal of research work published on Intention Analysis in the last 20 years.

So, we’re really happy to be one of the first research teams in ‘recent times’ to delve into the subject again.

We’re also really thrilled to be able to let you know that we’ve just been allowed to demonstrate our work on Intention Analysis at the COLING 2012 conference which will be held in Bombay (now officially known as Mumbai).

I hope I shall get to meet many of you in Bombay in a couple of weeks.

The theory that defines and shapes our work on Intention Analysis is known as ‘Speech Act Theory’.

One of the earlier philosophers to work on it was John Rogers Searle.

He augmented the theory with the concept of Intentional States.  (Incidentally, Intentional States are not even defined in the Wikipedia).

According to Searle, Intentional States could be either Beliefs or Desires.

He differentiated Beliefs from Desires by their direction of fit.

The direction of fit of an intentional state is said to be ‘mind-to-world’ if through the performance of the speech act, a mental state is established, revealed or altered.

The direction of fit of a speech act or intentional state is said to be ‘world-to-mind’ if the performance of the speech act alters the state of the world.

So, sentiment analysis is all about Beliefs.  The direction of fit is mind-to-world.  You see things in the world, and form opinions about them.

Intention analysis on the other hand is all about Desires.  The direction of fit is world-to-mind.  You try to fit the world to a model of how the world should be that resides in your mind.

If you would like to learn more, you can find our paper here:  www.aiaioo.com/publications/coling2012.pdf

Discovering intent for retargeting

Text-based Intention Analysis

At Aiaioo Labs, we have been developing text analytics tools for the discovery of intent for a number of years, as explained in the following articles:

1. Intentions and Opinions
2. Prior Work on Intentions

In recent months and years, we have seen other firms come up with similar technologies, studies and initiatives:

1. SocialNuggets offers a study on the ROI of intent analysis
2. Solariat has been trying to convince Facebook to use intention analysis for ad placement
3. WisdomTap did a study on the power of intent analysis with us

These are all text-based studies, using the content of conversations on social media to discover intentions.

However, the most recent attempts at improving the targeting of ads have focused on methods for discovering intent without using any unstructured information.  Below, we look at two of the methods.

One of the methods is advertisement retargeting.

Retargeting

Retargeting involves showing ads to customers based on intent discovered from past visits to sales portals.  For instance, if you visited Amazon’s books section, you have demonstrated the intent to purchase books.  It’s clear as crystal, and there is no text analysis involved.

Now if Amazon tells Facebook that a particular user visited Amazon’s books section, Facebook can use the information to show suitable Amazon ads to that user, even after the user has left Amazon.  That is called retargeting.  It is in essence a kind of intention analysis, but without the use of text.

Here is an article about retargeting on Facebook:

It’s Become Tragically Clear That Facebook Chased The Wrong Business For Years

Another method is the use of special purpose social networks.

Special Purpose Social Networks

When most people go on LinkedIn, they have careers or business contacts on their minds.  So, LinkedIn might work well for job ads or B2B branding exercises.

This article on Forbes talks about this type of intent analysis:  Why Consumer Intent Drives The Value Of Social Networks

It’s very interesting to see wholly new ways to guess at social networking users’ intentions.

Text-Based

• Advantages:  doesn’t need data from outside the website
• Disadvantages:  might be perceived as an invasion of privacy, needs conversations, low volume, low accuracy

Retargeting

• Advantages:  high volume, high accuracy, no privacy issues, needs no conversations
• Disadvantages:  needs data from outside the website

Special purpose networks

• Advantages:  high volume, no privacy issues, needs no conversations
• Disadvantages:  low accuracy, the space of intentions that can be assumed is limited per network

What can Game Theory learn from Social CRM and vice versa?

Customer service can now be delivered through social media channels (Social CRM) in addition to traditional closed channels like feedback forms (closed in the sense that the issues described are not made public).  But there are benefits and costs to using social media as a channel for receiving complaints.

Complaints voiced on social media about a product might greatly affect a potential customer’s decision to buy the product.  So, brand owners have a clear incentives to respond to and resolve complaints on social media.

On closed channels like call centers or customer service email addresses since there is no danger of negative feedback being seen by and influencing another person’s buying decisions.

So, social media gives users a greater bargaining power compared to traditional complaint channels. This gives brand owners one more incentive to resolve complaints – it is no longer just a question of a complainor’s satisfaction, but also of an unknown potential buyer’s impressions of the brand.

So, there is a clear incentive for firms to respond to customer complaints on social media channels.

However, a recognition of the greater bargaining power on social media combined with the greater ease of voicing a complaint on a social media channels might give users an incentive to choose social media channels instead of closed channels to voice negative sentiments.

So, if a service provider does not listen and respond to complaints voiced on social media, there might just be fewer expressions of negative sentiment on social media in the first place.

So, you can argue both for and against using social media channels to handle customer complaints. In this whitepaper we attempt to study the opposing motivations using game theory.

http://aiaioo.com/whitepapers/game_theory_investigations.pdf

How can you find out who wants to buy your products?

One way to understand a brand’s strengths is to see how often people say they want to buy that brand, on Twitter or other social media.

We have a demo of Twitter analysis for finding customers.

The following link takes you to a live demonstration of purchase intention detection being run on a Twitter stream:

http://www.aiaioo.com/demos/demo_use_case.html

What you will see on the page is tweets of people wanting to purchase something.

It is a fully automatic way of learning how many people want to buy your products, and who they are.