Collective Intelligence – Part 5: Extracting Intelligence from Tags

This is the fifth of a series of posts on the topic of programming Collective Intelligence in web applications. This series of posts will draw heavily from Santam Alag’s excellent book Collective Intelligence in Action.

These posts will present a conceptual overview of key strategies for programming CI, and will not delve into code examples. For that, I recommend picking up Alag’s book. You won’t be disappointed!

Click on the following links to access previous posts in this series:

• Part 1: Introduction
• Part 2: Basic Algorithms
• Part 3: Gathering Intelligence from User Interaction
• Part 4: Calculating Similarity

Introduction

So far in this series of posts, we’ve been introduced to some basic algorithms in CI, looked at various forms of user interaction, and explored how we used term vectors and similarity matrices to calcuate the similarity between users, items, and items and users. In this post, we’ll explore how to gather intelligence from tags.

Alag introduces the topic of gathering intelligence from tags as follows:

Users tagging items—adding keywords or phrases to items—is now ubiquitous on the web. This simple process of a user adding labels or tags to items, bookmarking items, sharing items, or simply viewing items provides a rich dataset that can translate into intelligence, for both the user and the items. This intelligence can be in the form of finding items related to the one tagged; connecting with other users who have similarly tagged items; or drawing the user to discover alternate tags that have been associated with an item of interest and through that finding other related items.

With that introduction, let’s begin.

Introduction to Tagging

Quoting Alag:

Tagging is the process of adding freeform text, either words or small phrases, to items. These keywords or tags can be attached to anything in your application—users, photos, articles, bookmarks, products, blog entries, podcasts, videos, and more.

[Previously] we looked at using term vectors to associate metadata with text. Each term or tag in the term vector represents a dimension. The collective set of terms or tags in your application defines the vocabulary for your application. When this same vocabulary is used to describe both the user and the items, we can compute the similarity of items with other items and the similarity of the item to the user’s metadata to find content that’s relevant to the user.

In this case, tags can be used to represent metadata. Using the context in which they appear and to whom they appear, they can serve as dynamic navigation links.

In essence, tags enable us to:

1. Build a metadata model (term vector) for our users and items. The common terminology between users and items enables us to compute the similarity of an item to another item or to a user.
2. Build dynamic navigation links in our application, for example, a tag cloud or hyperlinked phrases in the text displayed to the user.
3. Use metadata to personalize and connect users with other users.
4. Build a vocabulary for our application.
5. Bookmark items, which can be shared with other users.

Content-based vs. Collaborative-based Metadata

Alag emphasizes the distinction between content-based and collaborative-based sources of metadata. Quoting Alag:

In the content-based approach, metadata associated with the item is developed by analyzing the item’s content. This is represented by a term vector, a set of tags with their relative weights. Similarly, metadata can be associated with the user by aggregating the metadata of all the items visited by the user
within a window of time.

In the collaborative approach, user actions are used for deriving metadata. User tagging is an example of such an approach. Basically, the metadata associated with the item can be computed by computing the term vector from the tags—taking the relative frequency of the tags associated with the item and normalizing the counts.

When you think about metadata for a user and item using tags, think about a term vector with tags and their related weights.

Categorizing Tags based on how they are generated

We can categorize tags based on who generated them. There are three main types of tags: professionally generated, user-generated, and machine-generated.

Professionally generated Tags

Again quoting Alag:

There are a number of applications that are content rich and provide different kinds of content—articles, videos, photos, blogs—to their users. Vertical-centric medical sites, news sites, topic-focused group sites, or any site that has a professional editor generating content are examples of such sites.

In these kinds of sites, the professional editors are typically domain experts, familiar with content domain, and are usually
paid for their services. The first type of tags we cover is tags generated by such domain experts, which we call professionally generated tags.

Tags that are generated by domain experts have the following characteristics:

• They bring out the concepts related to the text.
• They capture the associated semantic value, using words that may not be found in the text.
• They can be authored to be displayed on the user interface.
• They can provide a view that isn’t centered around just the content of interest, but provides a more global overview.
• They can leverage synonyms—similar words.
• They can be multi-term phrases.
• The set of words used can be controlled, with a controlled vocabulary.

Professionally generated tags require a lot of manpower and can be expensive, especially if a large amount of new content is being generated, perhaps by the users. These characteristics can be challenging for an automated algorithm.

User-generated Tags

Back to Alag:

It’s now common to allow users to tag items. Tags generated by the users fall into the category of user-generated tags, and the process of adding tags to items is commonly known as tagging.

Tagging enables a user to associate freeform text to an item, in a way that makes sense to him, rather than using a fixed terminology that may have been developed by the content owner or created professionally.

[For example, considering the tagging processes] at del.icio.us. Here, a user can associate any tag or keyword with a URL. The system displays a list of recommended and popular tags to guide the user.

The use of users to create tags in your application is a great example of leveraging the collective power of your users. Items that are popular will tend to be frequently tagged. From an intelligence point of view, for a user, what matters most is which items people similar to the user are tagging.

User-generated tags have the following characteristics:

• They use terms that are familiar to the user.
• They bring out the concepts related to the text.
• They capture the associated semantic value, using words that may not be found in the text.
• They can be multi-term phrases.
• They provide valuable collaborative information about the user and the item.
• They may include a wide variety of terms that are close in meaning.

User-generated tags will need to be stemmed to take care of plurals and filtered for obscenity. Since tags are freeform, variants of the same tag may appear. For example, collective intelligence and collectiveintelligence may appear as two tags.

[Additionally,] you may want to offer recommended tags to the user based on the dictionary of tags created in your application and the first few characters typed by the user.

Machine-generated Tags

Tags or terms generated through an automated algorithm are known as machine-generated tags. Alag provides several examples in his book of extracting tags using an automated algorithm – for example, generating tags by analyzing the textual content of a document.

Again from Alag:

An algorithm generates tags by parsing through text and detecting terms and phrases.

Machine-generated tags have the following characteristics:

• They use terms that are contained in the text, with the exception of injected synonyms.
• They’re usually single terms—Multi-term phrases are more difficult to extract and are usually done using a set of predefined phrases. These predefined phrases can be built using either professional or user-generated tags.
• They can generate a lot of noisy tags—tags that can have multiple meanings based on the context, including polysemy and homonyms.—For example, the word gain can have a number of meanings—height gain, weight gain, stock price gain, capital gain, amplifier gain, and so on. Again, detecting multiple-term phrases, which are a
  lot more specific than single terms, can help solve this problem.

In the absence of user-generated and professionally generated tags, machine-generated tags are the only alternative. This is especially true for analyzing user-generated content.

How to leverage Tags in your application

Alag leads off this section of his book with the following:

It’s useful to build metadata by analyzing the tags associated with an item and placed by a user. This metadata can then be used to find items and users of interest for the user. In addition to this, tagging can be useful to build dynamic navigation in your
application, to target search, and to build folksonomies. In this section, we briefly review these three use cases.

I’m not going to explore the specific use cases that Alag covers in his book. Again, you know where to find the details. 🙂

Other topics

Alag concludes his chapter on extracting intelligence from tagging with:

1. An example that illustrates the process of extracting intelligence from user tagging, and
2. Thoughts on building a scalable persistence architecture for tagging

Exploring the tagging example and Alag’s thoughts on a persistence architecture for tagging is beyond the introductory scope of this post. Please see Alag’s book for more information.

In Summary

Hopefully this post has given you a bit of a flavor of how Tags are used to surface collective intelligence in a social web application. In the final post in this series, I’ll be exploring extracting intelligence from textual content.

Also in this series

• Collective Intelligence – Part 1: Introduction
• Collective Intelligence – Part 2: Basic Algorithms
• Collective Intelligence – Part 3: Gathering Intelligence from User Interaction
• Collective Intelligence – Part 4: Calculating Similarity