Qualitative Analysis

Early on, long before machine learning, we did content analysis.  We still did quantitative analytics, but only after we had structured our problem with qualitative methods.  In fact you always start with qualitative.  Here a good intro:

Qualitative before Quantitative: How Qualitative Methods Support Better Data Science Co-authored with Vicky Zhang in Medium.

Have you ever been embarrassed by the first iteration of one of your machine learning projects, where you didn’t include obvious and important features? In the practical hustle and bustle of trying to build models, we can often forget about the observation step in the scientific method and jump straight to hypothesis testing.

Data scientists and their models can benefit greatly from qualitative methods. Without doing qualitative research, data scientists risk making assumptions about how users behave. These assumptions could lead to:

Neglecting critical parameters,
missing a vital opportunity to empathize with those using our products, or
misinterpreting data.

In this post, we’ll explore how qualitative methods can help all data scientists build better models, using a case study of Indeed’s new lead routing machine learning model which ultimately generated several million dollars in revenue.

What are qualitative methods and how are they different from quantitative methods?
Few data scientists are formally trained in qualitative methods. They’re more deeply familiar with quantitative methods like A/B testing, surveys, and regressions. Quantitative methods are great for answering questions like “How much does the average small business spend on a job posting?”, “What are the skills that make someone a data scientist?”, or even “How many licks does it take to get to the center of a Tootsie roll pop?” (The answer is 3. Three licks.)

But there are some questions that quantitative methods can’t answer, such as “Why do account executives reach out to this lead instead of that lead?” or “How do small businesses make the decision to sponsor or not sponsor a job?” Or the truly deep question: “Why do you want to get to center of the Tootsie roll pop?”

To answer these questions, qualitative researchers rely on methods like in-depth interviews, participant observation, content analysis and usability studies. These methods involve more direct contact with who and what you’re studying. They allow you to better understand how and why people do what they do, and what kinds of meaning they ascribe to different behaviors.

Put another way, quantitative methods can tell you the “what”, the “how much”, or “how often”; qualitative methods can tell you the “why” or the “how”. ... "

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A Look at Text Classification

From an introduction, to simple methods, to depth  This was an area we used extensively for 'content' analysis to understand consumer responses.  Most recently have looked at it as a method of classifying written comments in compliance documents.  New methods in machine learning have improved it considerably.

Text Classification & Sentiment Analysis tutorial / blog   
Posted by Ahmet Taspinar  

We all know that with Machine Learning you can automatically classify text documents or analyze its subjectivity. We've just released a guide that gives a brief introduction to Text Classification. 
It cover the three most used classifiers; Naive Bayes, Maximum Entropy and Support Vector Machines and will give practical examples in the form of the sentiment analysis of book reviews.  ... "
 

Topic Modeling in R

Topic Modeling.  A kind of textual analysis we did long before R, using cluster based 'Content Analysis'.  An  examination and R code for how this can be done in the modern era. An example of unstructured text analytics.     Their definition:  " .... A statistical approach for discovering “abstracts/topics” from a collection of text documents based on statistics of each word. In simple terms, the process of looking into a large collection of documents, identifying clusters of words and grouping them together based on similarity and identifying patterns in the clusters appearing in multitude. .. " 

Dealing with Unstructured Data

A look at the value of unstructured data.  Worth a read.  Text is structured because cognitive beings created it.  But has structure that is driven by a number of influencing factors, that should have useful value.  Good read.  Good example.  Reminds me of our own experience in Content Analytics.

In CustomerThink: 
" ... What I want to focus on here is the nature of dealing with unstructured data. That’s where text analytics comes in. Text automation and analysis provides a way of harnessing unstructured information and incorporating it into insights and planning. I’ve been working with text for the last 12 years and have watched the field change a tremendous amount. In the earliest commercial offerings, text software was very time-consuming. It was heavily geared toward predictive modeling and typically used methods based on statistical and machine learning. Capabilities were far less robust for the most common analytic application today – survey verbatim classification through building taxonomies. (I remember working with one insurer whose team spent over 3,000 man hours building out text analytics just for categorizing claims notes). ... " 

Google NGram Viewer

While considering some unstructured text analysis the Google Ngram viewer came to mind.  The system " .... charts frequencies of any word or short sentence using yearly count of n-grams found in the sources printed between 1800 and 2012 ,,, ".  Based on Google Books or other collections of texts.   In multiple languages.  Addictive for the linguistically inquisitive. We had proposed something for a textual 'content analysis' project where we could have used the idea.   In the Wikipedia.  Can anyone point me me its use for text analytics?  This made me recall it had been posted on quite a few times here before, see the tag below.  See also the book: Uncharted: Big Data as a Lens on Human Culture by Erez Aiden and Jean-Baptiste Michel. 

On the Mess of Unstructured Analytics

Dealing with the mess.  Agree that this should not be.  But we have more different kinds of unstructured data today than we ever had.  In face we dealt with it since the content analytics days.   Some very good points are made here.  But I warn that organizational changes are harder to make than statistical choices.

"   .... There’s a reason for this: Because the data is poorly managed to begin with. Businesses are treating analytics as a separate business function from data governance, when it’s actually fundamentally dependent on it. Analysis occurs downstream; so by neglecting initial information governance infrastructure and practices, the enterprise is essentially sampling tiny random buckets of data from a whitewater river of information.

Many firms struggle to manage or even understand what sort of unstructured content they even have, let alone begin to effectively manage it. History is partially to blame, to be sure; most attempts at managing unstructured content were hastily prompted by waves of regulatory and legal reform that demanded immediate action. A reactive response was triggered, and many of those initial “band-aid” information management fixes remain in place today. Simply scratching beneath the surface often reveals a tangled mess of siloed    .... " 

Recommendation Engines for Newspapers

Language Log describes the new NY Times article recommendation engine.  This piece is interesting because it discusses the problem from a linguistic, rather than entirely from a computer science direction.   Newspapers used to recommend by simple placement on a page, now they have the opportunity to recommend online in new ways.    Will this slow their demise?  Consider the links to storytelling and content analytics.  But does this lead more directly to pre established narratives?

" ... The New York Times publishes over 300 articles, blog posts and interactive stories a day. ... Refining the path our readers take through this content — personalizing the placement of articles on our apps and website — can help readers find information relevant to them, such as the right news at the right times, personalized supplements to major events and stories in their preferred multimedia format. ... " 

Content Analytics Re-Emerges

This was an approach we called 'Content Analytics' a long time ago, when the data we analyzed came from big spools of tape.  I had thought the term had gone away, to be replaced by 'Bigger' things.   So was glad to see this piece.  It now has many more tools, visual and analytical you can attach to get better results.   Now as then it has a more focused business direction.    Aimed at unstructured data, but even that term changes as we can better add our own structure.  In Datafloq:

" .... Content analytics can be defined as unlocking business value from unstructured content via semantic technologies to find answers to important questions or discover causes to certain trends. Companies can use content analytics to understand the content that is created, how it is used, the context it is in and the nature of that content. Content analytics is all about unstructured data and it can be used to explain trends in structured data and provide valuable insights to organisations.

Content analytics is especially relevant for organisations where knowledge is at the core of their business and in that case ordinary business intelligence is not sufficient anymore. Knowing who read what content, when, how often it was shared, number of clicks, location of visitors, etc. is not sufficient anymore. You should also care about whether the content is actually useful to your audiences and that it serves the objective it was intended for. Content related trends or insights revealing information such as what content caused a drop in sales can help make better content and thus drive growth or revenue. It is not about having more content; it is about having better content. ... "