The business value of unstructured data can be left unexploited when data mining is executed with an inadequate,
static data model designed for structured data. The authors of this chapter excerpt detail a three-phase process intended to make data mining and analyzing unstructured data more streamlined.
In our journey of discovery, we have seen one mistake made repeatedly. We have seen static business models and static data models try to be used to model inherently dynamic business processes, particularly at the point of interaction. For example, virtually every customer relationship management system we have come across has a manual classification scheme (or taxonomy) that is meant to be used by the service agent to classify the nature of the customer interaction. This approach has two major flaws. First, as soon as the classification scheme is published, it is out of date, because interactions with your customers are unpredictable and continually changing. Second, even if the classification scheme was representative of your customer interactions, it is unreasonable to expect any number of service agents to classify their interactions with their customers in a consistent way and with high quality. This very often makes such classification data completely useless, or, more dangerously, misleading. This issue is true throughout the business ecosystem where unstructured information exists.
An adaptable data model is critical when incorporating unstructured information mining. One problem commonly encountered is that the analysis typically leads to more questions. In business intelligence or data mining, if the data model is not designed to handle the new question, the data model must be modified and the data manipulated and reloaded, which is often a difficult and cumbersome process, many times taking months. This problem is compounded even more with unstructured information because of its very nature. It is important to be able to add or enhance existing taxonomies, classifications, or extracted data as the information leads you through the discovery process.
Additionally, it is important to combine the right mix of algorithmic assistance with domain expertise. We have found that most people naively want to push a button and magically receive the answer. However, we have come to the conclusion that, particularly with unstructured information, the analytical process cannot be fully automated. Even for structured information, it typically needs an analyst in the loop to interpret the results. For unstructured information, an analyst is required in the loop to help guide the process with a combination of algorithmic assistance, a useful set of metrics to assist in the interpretation, and the analyst's domain expertise. The key is to make efficient use of this expensive and scarce resource, not to eliminate it entirely.
A common analytical methodology
We struggled for some time and went through multiple iterations before we settled on a common analytical methodology that spans domains, business objectives, and information sources. Our method has three major phases: Explore, Understand, and Analyze. Each of these phases leverages different capabilities that build on each other. However, it is not always necessary to use every phase or capability. For very large information collections, we have developed the capabilities in Explore, and we will discuss this in Chapter 5, "Mining to Improve Innovation." Most of this book will concentrate on the Understand and Analyze phases, which are the unique differentiators and the key to unlocking the business value of unstructured information in Mining the Talk. In this section, we introduce each of these concepts and describe what they entail.
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Explore. Many times we are dealing with very large repositories of information. Depending on our information source and our business objective, not all of the information will be relevant. For example, if you are interested in analyzing the Web to understand issues around a specific brand, you only need the portion of the Web that pertains to that brand. If you are analyzing patents related to a technology area, you only need the patents that are relevant to that area. We use a combination of techniques to locate the relevant set of information from a larger set. With structured information, we can use various queries; for unstructured information, we can use search, and we can combine them in different ways using various set operations. We call this process Explore.
Queries. We use query as the term to describe how we use structured fields in a database to select the subset of information that is of interest. For example, we can select customers based upon their location, the product they have purchased, or the time frame that we wish to investigate. We can select patents based upon the assignee, the inventor, or the classification code. These are all typically structured fields that are stored in a database. These types of queries are quite simple to perform using the standard SQL query language to find the sub-collections of interest. This technique is very powerful and effective, given you have the appropriate attributes in the database and know which of their values will select the subset that is relevant to address the issue being analyzed.
Search is the process of finding those documents that contain specific words or phrases in their unstructured text. We use search as the means to find collections of documents that have concepts of interest within them, rather than to find individual documents. Although it is a valuable tool, search is not the solution to all problems. The use of language does not always lend itself to easily disambiguate concepts. Some words have more than one meaning, known as homonyms. For example, using "shell" as a query will likely return information on sea shells, Shell Oil, Unix shell, egg shells, and many others. Disambiguation is one problem, but coverage is another. Some meanings can be described with more than one word, known as synonyms. For example, we have found that valium has more than 150 unique names—have fun typing that query.
Set operations. Because in many cases no single query or search is sufficient to get to the optimally desired collection for deeper analysis, we have found it necessary to be able to perform set operation on collections. The most commonly used operations are join and intersect. Joins are useful in combining multiple searches for synonyms. Intersection is useful when you are looking for the subset that has two attributes that could be from either the structured or unstructured fields. In some cases, when the result of a combination of queries and searches is still too large to effectively analyze in a reasonable time, sampling techniques may be used to select a statistically valid subset.
Recursion and expansion. Results of queries and searches can be used as input to subsequent Explore operations. This allows us to refine the subject of our mining study incrementally as we learn more about the data. Also, we can use query expansion to take the results of a query done on a subset of the data and apply it to the entire data collection.
The result of the Explore phase is a collection of information that covers the topic of interest. The Understand phase is about discovering what the information contains. We have developed a unique method of creating structure from unstructured information through the process of taxonomy generation and refinement. We use a combination of practical steps, statistical techniques, algorithms, and a methodology for editing taxonomies that allows for the flexible capture of domain expertise and business objectives. We call this process Understand. The Understand process works in two directions: the analyst understands the underlying structure inherent in the unstructured information, and the models captured as a result of the analyst's edits represent an understanding of domain knowledge and business objectives.
Statistics are fundamental to our Understand process. We are all familiar with the idea of summarizing numerical data with statistical techniques. For example, a grade point average is a way to summarize your overall academic performance. It doesn't tell you everything, but most people have agreed that it is a pretty good indicator. What about something more complex, like a sporting event? Pick your favorite sport—whether it is baseball, basketball, tennis, or football—and there are usually various ways to summarize the game or match that allow you to understand the essence of what transpired. Such summaries are no substitute for watching the game, but they can convey a lot of information about the game in a very small space.
Partitioning. If you have a large body of text, there is probably one or more natural ways to partition it into smaller sections. A book naturally falls into chapters, and each chapter into paragraphs, and each paragraph into sentences, just as a baseball game has innings and innings have outs. Breaking a large document into smaller entities makes it much easier to summarize the message of the text as a whole, because it makes statistics possible. If we try to summarize a baseball game without breaking it down by innings and outs, we are left with only the final score. But if we can break down the game into innings or atbats and measure what happened during each of these smaller units (e.g., hits, walks, outs), then we can create meaningful statistics such as Earned Run Average or On Base Percentage.
There may be many suitable ways to do partitioning, each with its own advantage. However, the best methods for partitioning are those that produce a section that talks about only one concept with respect to the questions we want answered. The level of granularity should roughly match that of the desired business result. The analogy in baseball is that we measure innings for pitchers and at-bats for batters. The different levels of granularity make sense for different kinds of outcomes that need to be measured. Similarly, if we want to understand the issues for which customers are calling into a call center, then individual problem records, which may span multiple calls, are the right partitioning. On the other hand, if we wish to understand better what affects customer satisfaction, we may decide to analyze each individual call record. A customer might be both satisfied and dissatisfied during the course of resolving an issue, and we want to isolate te interactions in order to analyze the underlying causes.
Feature selection. Once the partitioning granularity is properly adjusted, we need to decide what events we are going to measure and what statistics we will keep. In a baseball box score, we don't measure everything about the game. For example, we don't know the average number of swings each batter took, or the number of pitches each pitcher threw. We could measure these things if they were important to us, but that level of detail is not interesting to the average baseball fan. Similarly in statistical analysis of text, we could measure the average number of times each letter of the alphabet occurs. We could measure the average word length, or the number of words in sentences. In fact, such statistics are used as a means for roughly measuring how "readable" a section of text will be for readers of various grade levels. However, these kinds of statistics are not helpful to answer typical business questions, such as "What are my customers most unhappy about?" So what are the right things to measure about each text example? The answer is, it all depends. It depends on what we want to learn and what kind of text data we are dealing with. Word occurrence is a good place to start for most types of problems, especially those where you don't have much specific domain knowledge to draw upon and where the language of the documents is fairly general. When the text is more technically dense or focused on a very specialized area, then it may make sense to also measure sequences of words, also known as phrases, to get a more precise kind of statistic.
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We use word and phrase occurrence as the features of a document. However, we don't use every word and phrase, because there can be a very large number of them and they are not all meaningful or useful. We use a combination of techniques to reduce the feature space to a more manageable size. We eliminate non-content–bearing words, called stopwords, such as "and" and "the." We also remove repetitive or structural phrases (we call them stock phrases). If every document contains "Copyright IBM" or "IBM Confidential," then it can safely be removed. We also combine features using a synonym list. This can be done manually where deemed appropriate or automatically through a technique called stemming. Stemming allows "jump," "jumping," and "jumped" to be treated as one. There are also various domain-specific synonym lists that can be used where stemming will fall short. Finally, we remove features that occur infrequently in the document collection because these tend to have little value in creating meaningful categories. Once we have reduced the features to a manageable size, we can use this to create summary statistics for each document. We call the collection of all such statistics for every document in a collection the vector space model.
Clustering. We use clustering to quickly and easily seed the process of taxonomy generation. Clustering is an algorithmic attempt to automatically group documents into thematic categories. These thematic categories, which together constitute a taxonomy, give an overview of what information the document collection contains. There are many different clustering algorithms that could be used, and our approach could support them all. However, we have relied heavily on variations of the k-means algorithm, because it is fast and does a reasonable job. We have also developed our own algorithm, which we call intuitive clustering, that we also employ.
Taxonomy editing. Clustering is a wonderful tool, but we rarely find it to be sufficient. No matter how good the algorithmic approach to clustering becomes, it cannot embed the nuance of business objectives and the variations of language from different information sources within an algorithm. This is the critical missing element that our method incorporates. We have developed a unique set of capabilities that allow for an analyst or domain expert to quickly assess the strengths and weaknesses of a taxonomy and easily make the changes necessary to align the taxonomy with business objectives.
Analyst knowledge about the purpose of the taxonomy trumps every other consideration. Thus, a category may be created by an analyst for reasons that have nothing to do with text features. An example would be a category of "recent" documents—those created most recently out of all the documents in the corpus. Depending on the business analysis goals, such a category may be very important in helping to understand emerging trends and issues.
Ideally, the name of a category should describe exactly what makes the category unique. An analyst may decide to change a system-generated name to one that is better aligned with the analyst's view of what the category contains. This category renaming process thus becomes an important way that domain expertise is captured.
In addition to the name, a category can also be described by choosing examples that best summarize the overall content. We describe these as "Typical Examples" because they are selected by virtue of having all or most of the features that typify the documents in the category as a whole. Using the vector space model, it is possible to automatically compare examples and select those that have the most typical content. By reading and understanding typical examples, it is possible for the analyst to make sense of a large collection of documents in a relatively short period of time.
It is also important to measure the variation within a category of documents. If there is a statistically large variation among the documents within a category, this may indicate that the category needs to be split up, or subcategorized. We call the metric that measures within category variation cohesion. Additionally, it is important to measure the similarity between categories. We call this distinctness. Categories with low distinctness scores indicate a potential overlap with another category. This overlap may indicate the need to merge two or more categories together.
The categories created using clustering and summarized with various statistics can also be edited based on this understanding. This is where analysts adds their domain knowledge and awareness of the business problem to be solved to the results—creating categories that are more meaningful.
There are many kinds of editing that are typically employed, at all levels of the text categorization. Categories can be merged or deleted. They can be created wholesale from documents matching individual words, phrases, or features. Categories can be edited—splitting off subsets of a category to create new categories. Documents can be selectively removed from one category and placed in another.
The taxonomy editing process can be thought of as the human expert training the computer to understand concepts that are important to the business. There may be many different types of categorizations that can be created on the same set of data, each representing a different important aspect of the information to the enterprise.
Visualizations. The visual cortex occupies about one third of the surface of the cerebral cortex in humans. It would be a shame to waste all of that immense processing power during the Understand process. We employ visualizations of taxonomies to create pictures of the information that the human brain can process in order to locate areas of special interest that contain patterns or relationships. There are many types of visualizations that can be used to show relationships in structured and unstructured information. Scatter plots, trees, bar graphs, and pie charts can all help in the process of understanding the information, and in modifying taxonomies to reflect business objectives.
The vector space model of feature occurrence in documents is the primary data source for automatically calculating visual representations of text. Using this representation, a document becomes not just words, but a position or point in high-dimensional space. Given this representation, a computer can "draw" a set of documents and allow a human analyst to explore the text space in much the same way an astronomer explores the galaxy of stars and planets.
At the end of the Understand phase, we have one or more taxonomies that represent characteristics of the unstructured information, along with a feature set that describes the individual documents that make up each taxonomy. But a taxonomy by itself rarely achieves the business objectives of mining unstructured information. The final step is to take combinations of structured and unstructured information and look for trends, patterns, and relationships inherent in the data and use that to make better business decisions. We call this process Analyze.
Trending. Timing is everything in comedy, in life, and in business. Knowing how categories occurred in the data stream over time will often reveal something interesting about why that category occurs in the first place. Trend analysis in data miningis also useful for detecting spikes in categories as well as in predicting how categories will evolve in the future. Trending can be interesting from a historical perspective, but it is usually most valuable when used to detect emerging events. If you can detect a problem in your business before it costs you a lot of money, that goes straight to the bottom line. If you can spot a trend before your competition, you have a leg up.
Correlations. Taxonomies capture the concepts embedded in unstructured information. Cooccurrence analysis reveals hidden relationships between these concepts and other attributes or between categories of different taxonomies. For example, we can look for a relationship between technology areas and companies to see where our competition is investing. Or we can find a correlation between a specific factory and a certain kind of product defect.
A correlation is based on the simple idea that two different phenomenon have occurred together more than expected. For example, if 100 customers who talked to a specific call center representative ended up dissatisfied with their overall customer experience, then depending on the total percentage of unsatisfied callers and the total percentage of calls that particular representative took, we could calculate whether there was a correlation between dissatisfaction and talking to this representative. Keep in mind that, even if there is such a correlation, it doesn't mean that this representative is actually responsible for the poor customer satisfaction. It could be that this person only works during weekends and that people who call on the weekends are generally more dissatisfied. This example serves to show that correlations are not causes. They are simply indicators of potential explanations that should be explored further. Think of them as "leading indicators" of business insights.
Classification. One you have a taxonomy that models an important aspect of the information, it is important to be able to apply this classification scheme to new unstructured data. Many classification algorithms exist, and we have incorporated a large variety of them into our approach, allowing us to select the best algorithm for a given taxonomy and information collection. The specifics of how we do text classification is a more technical subject that is beyond the scope of this book. However, the general approach is to pick the algorithm that most accurately represents each category, based on a random sampling of the documents in that category being used to test the accuracy of each modeling approach.
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