Posts tagged text analysis
Poll: What Other Countries Think of Trump’s Immigration Order

Text Analytics PollTM Shows Australians, Brits, and Canadians  Angry About Executive Order Temporarily Barring Refugee (Part II of II)In my previous post, we compared text analysis of results from an open-ended survey instrument with a conventional Likert-scale rating poll to assess where 3,000 Americans really stand on President Trump’s controversial executive order temporarily barring refugees and people from seven predominately-Muslim countries from entering the U.S.

Today, we’re going to share results from an identical international study that asked approx. 9,000 people—3,000 people from each of three other countries—what they think about the U.S. immigration moratorium ordered by President Trump.

But first, a quick recap…

As I noted in the previous post, polling on this issue has been pretty consistent insomuch as Americans are closely divided in support/opposition, but the majority position flips depending on the poll. Consequently, the accuracy of polling has again been called into question by pundits on both sides of the issue.

By fielding the same question first in a multiple-choice response format and a second time providing only a text comment box for responses, and then comparing results, we were able to not only replicate the results of the former but gain a much deeper understanding of where Americans really stand on this issue.

Text analysis confirmed a much divided America with those opposing the ban just slightly outnumbering (<3%) those who support the order (42% vs 39%). Almost 20% of respondents had no opinion or were ambivalent on this issue.

Bear in mind that text analysis software such as OdinText enables us to process and quantify huge quantities of comments (in this case, more than 1500 replies from respondents using their own words) in order to arrive at the same percentages that one would get from a conventional multiple-choice survey.

But the real advantage to using an open-ended response format (versus a multiple-choice) to gauge opinion on an issue like this is that the responses also tell us so much more than whether someone agrees/disagrees or likes/dislikes. Using text analytics we uncovered people’s reasoning, the extent to which they are emotionally invested in the issue, and why.

Today we will be looking a little further into this topic with data from three additional countries: Australia, Canada and the UK.

A note about multi-lingual text analysis and the countries selected for this project…

Different software platforms handle different languages with various degrees of proficiency. OdinText analyzes most European languages quite well; however, analysis of Dutch, German, Spanish or Swedish text requires proficiency in said language by the analyst. (Of course, translated results, including and especially machine-translated results, work very well with text analytics.)

Not inconspicuously, each of the countries represented in our analysis here has an English-speaking population. But this was not the primary reason that we chose them; each of these countries has frequently been mentioned in news coverage related to the immigration ban: The UK because of Brexit, Australia because of a leaked telephone call between President Trump and its Prime Minister, and Canada due to its shared border and its Prime Minister’s comments on welcoming refugees affected by the immigration moratorium.

Like our previous U.S. population survey, we used a nationally-representative sample of n=3000 for each of these countries.

Opposition Highest in Canada, Lowest in the UK

It probably does not come as a surprise to anyone who’s been following this issue in the media that citizens outside of America are less likely to approve of President Trump’s immigration moratorium.

I had honestly expected Australians to be the most strongly opposed to the order in light of the highly-publicized and problematic telephone call transcript leaked last week between President Trump and the Australian Prime Minister (which, coincidentally, involved a refugee agreement). But interestingly, people from our close ally and neighbor to the north, Canada, were most strongly opposed to the executive order (67%). The UK had proportionately fewer opposing the ban than Australia (56% vs. 60%), but the numbers of people opposed to the policy in both countries significantly lagged the Canadians. Emotions Run High Abroad Deriving emotions from text is an interesting and effective measure for understanding people’s opinions and preferences (and more useful than the “sentiment” metrics often discussed in text analytics and, particularly, in social media monitoring circles).

The chart below features OdinText’s emotional analysis of comments for each of the four countries across what most psychologists agree constitute the eight major emotion categories:

We can see that while the single highest emotion in American comments is joy/happiness, the highest emotion in the other three countries is anger. Canadians are angriest. People in the UK and Australians exhibit somewhat greater sadness and disgust in their comments. Notably, disgust is an emotion that we typically only see rarely in food categories. Here it takes the form of vehement rejection with terms such as “sickened,” “revolting,” “vile,” and, very often, “disgusted.” It is also worth noting that in cases, people directed their displeasure at President Trump, personally.

Examples:

"Trump is a xenophobic, delusional, and narcissistic danger to the world." – Canadian (anger) “Most unhappy - this will worsen relationships between Muslims and Christians.” – Australian (sadness) "It's disgusting. You can't blame a whole race for the acts of some extremists! How many white people have shot up schools and such? Isn't that an act of terror? Ban guns instead. He's a vile little man.” –Australian (disgust)

UK comments contain the highest levels of fear/anxiety:

"I am outraged. A despicable act of racism and a real worry for what political moves may happen next." – UK (fear/anxiety)

That said, it is also important to point out that there is a sizeable group in each country who express soaring agreement to the level of joy:

“Great move! He should stop all people that promote beating of women” – Australian (joy) “Sounds bloody good would be ok for Australia too!” – Australian (joy) “EXCELLENT. Good to see a politician stick by his word” – UK (joy) “About time, I feel like it's a great idea, the United States needs to help their own people before others. If there is an ongoing war members of that country should not be allowed to migrate as the disease will spread.” – Canadian (joy)

Majority of Canadians Willing to Take Refugee Overflow Given Canada’s proximity to the U.S., and since people from Canada were the most strongly opposed to President Trump’s executive order, this raised the question of whether Canadians would then support a measure to absorb refugees that would be denied entrance to the U.S., as Prime Minister Justin Trudeau appears to support.

(Note: In a Jan. 31 late-night emergency debate, the Canadian Parliament did not increase its refugee cap of 25,000.)

 

A solid majority of Canadians would support such an action, although it’s worth noting that there is a significant difference between the numbers of Canadians who oppose the U.S. immigration moratorium (67%) and the number who indicated they would be willing to admit the refugees affected by the policy.

When asked a follow-up question on whether “Canada should accept all the refugees which are turned away by USA's Trump EO 13769,” only 45% of Canadians agreed with such a measure, 33% disagreed and 22% said they were not sure.

Final Thoughts: How This Differs from Other Polls Both the U.S. and the international versions of this study differ significantly from any other polls on this subject currently circulating in the media because they required respondents to answer the question in a text comment box in their own words, instead of just selecting from options on an “agree/disagree” Likert scale.

As a result, we were able to not only quantify support and opposition around this controversial subject, but also to gauge respondents’ emotional stake in the matter and to better understand the “why” underlying their positions.

While text analysis allows us to treat qualitative/unstructured data quantitatively, it’s important to remember that including a few quotes in any analysis can help profile and tell a richer story about your data and analysis.

We also used a substantially larger population sample for each of the countries surveyed than any of the conventional polls I’ve seen cited in the media. Because of our triangulated approach and the size of the sample, these findings are in my opinion the most accurate numbers currently available on this subject.

I welcome your thoughts!

@TomHCAnderson - @OdinText

About Tom H. C. Anderson Tom H. C. Anderson is the founder and managing partner of OdinText, a venture-backed firm based in Stamford, CT whose eponymous, patented SAS platform is used by Fortune 500 companies like Disney, Coca-Cola and Shell Oil to mine insights from complex, unstructured and mixed data. A recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research, Anderson is the recipient of numerous awards for innovation from industry associations such as CASRO, ESOMAR and the ARF. He was named one of the "Four under 40" market research leaders by the American Marketing Association in 2010. He tweets under the handle @tomhcanderson.

Five Reasons to NEVER Design a Survey without a Comment Field

Marketing Research Confessions Part II - Researchers Say Open-Ends Are Critical!

My last post focused on the alarmingly high number of marketing researchers (~30%) who, as a matter of policy, either do not include a section for respondent comments (a.k.a. “open-ended” questions) in their surveys or who field surveys with a comment section but discard the responses.

The good news is that most researchers do, in fact, understand and appreciate the value of comment data from open-ended questions.

Indeed, many say feedback in consumers’ own words is indispensable.

Among researchers we recently polled:

  • 70% would NEVER launch tracker OR even an ad-hoc (66%) survey without a comment field
  • 80% DO NOT agree that analyzing only a subset of the comment data is sufficient
  • 59% say comment data is AT LEAST as important as the numeric ratings data (and many state they are the most important data points)
  • 58% ALWAYS allocate time to analyze comment data after fielding

In Their Own Words: “Essential”

In contrast to the flippancy we saw in comments from those who don’t see any need for open-ended survey questions, researchers who value open-ends felt pretty strongly about them.

Consider these two verbatim responses, which encapsulate the general sentiment expressed by researchers in our survey:

“Absolutely ESSENTIAL. Without [customer comments] you can easily draw the wrong conclusion from the overall survey.”

“Open-ended questions are essential. There is no easy shortcut to getting at the nuanced answers and ‘ah-ha!’ findings present in written text.”

As it happens, respondents to our survey provided plenty of detailed and thoughtful responses to our open-ended questions.

We, of course, ran these responses through OdinText and our analysis identified five common reasons for researchers’ belief that comment data from open-ended questions is critically important.

So here’s why, ranked chronologically in ascending order by preponderance of mentions and in their own words

 Top Five Reasons to Always Include an Open-End

 

#5 Proxy for Quality & Fraud

“They are essential in sussing out fraud—in quality control.”

“For data quality to determine satisficing and fraudulent behavior

“…to verify a reasonable level of engagement in the survey…”

 

#4 Understand the ‘Why’ Behind the Numbers

“Very beneficial when trying to identify cause and effect

“Open ends are key to understand the meaning of all the other answers. They provide context, motivations, details. Market Research cannot survive without open ends”

Extremely useful to understand what is truly driving decisions. In closed-end questions people tend to agree with statements that seem a reasonable, logical answer, even if they have not considered them before at all

“It's so critical for me to understand WHY people choose the hard codes, or why they behave the way the big data says they behave. Inferences from quant data only get you so far - you need to hear it from the horse’s mouth...AT SCALE!”

“OEs are windows into the consumer thought process, and I find them invaluable in providing meaning when interpreting the closed-ended responses.”

 

#3 Freedom from Quant Limitations

“They allow respondents more freedom to answer a question how they want to—not limited to a list that might or might not be relevant.”

“Extremely important to gather data the respondent wants to convey but cannot in the limited context of closed ends.”

“Open-enders allow the respondent to give a full explanation without being constrained by pre-defined and pre-conceived codes and structures. With the use of modern text analytics tools these comments can be analyzed and classified with ease and greater accuracy as compared to previous manual processes.”

“…fixed answer options might be too narrow.  Product registration, satisfaction surveys and early product concept testing are the best candidates…”

allowing participants to comment on what's important to them

 

#2 Avoiding Wrong Conclusions

“We code every single response, even on trackers [longitudinal data] where we have thousands of responses across 5 open-end questions… you can draw the wrong conclusion without open-ends. I've got lots of examples!”

“Essential - mitigate risk of (1) respondents misunderstanding questions and (2) analysts jumping to wrong conclusions and (3) allowing for learnings not included in closed-ended answer categories”

“Open ended if done correctly almost always generate more right results than closed ended.  Checking a box is cheap, but communicating an original thought is more valuable.”

 

#1 Unearthing Unknowns – What We Didn’t Know We Didn’t Know

“They can give rich, in-depth insights or raise awareness of unknown insights or concerns.”

“This info can prove valuable to the research in unexpected ways.”

“They are critical to capture the voice of the customer and provide a huge amount of insight that would otherwise be missed.”

“Extremely useful.  I design them to try and get to the unexpected reasons behind the closed-end data.”

“To capture thoughts and ideas, in their own words, the research may have missed.”

“It can give good complementary information. It can also give information about something the researcher missed in his other questions.”

“Highly useful. They allow the interviewee to offer unanticipated and often most valuable observations.”

 

Ps. Additional Reasons…

Although it didn’t make the top five, several researchers cited one other notable reason for valuing open-ended questions, summarized in the following comment:

“They provide the rich unaided insights that often are the most interesting to our clients

 

Next Steps: How to Get Value from Open-Ended Questions

I think we’ve established that most researchers recognize the tremendous value of feedback from open-ended questions and the reasons why, but there’s more to be said on the subject.

Conducting good research takes knowledge and skill. I’ve spent the last decade working with unstructured data and will be among the first to admit that while the quality of tools to tackle this data have radically improved, understanding what kind of analysis to undertake, or how to better ask the questions are just as important as the technology.

Sadly many researchers and just about all text analytics firms I’ve run into understand very little about these more explicit techniques in how to actually collect better data.

Therefore I aim to devote at least one if not more posts over the next few weeks to delve into some of the problems in working with unstructured data brought up by some of our researchers.

Stay tuned!

@TomHCAnderson

 

Ignoring Customer Comments: A Disturbing Trend

One-Third of Researchers Think Survey Ratings Are All They Need

You’d be hard-pressed to find anyone who doesn’t think customer feedback matters, but it seems an alarming number of researchers don’t believe they really need to hear what people have to say!

 

2in5 openends read

In fact, almost a third of market researchers we recently polled either don’t give consumers the opportunity to comment or flat out ignore their responses.

  • 30% of researchers report they do not include an option for customer comments in longitudinal customer experience trackers because they “don’t want to deal with the coding/analysis.” Almost as many (34%) admit the same for ad hoc surveys.
  • 42% of researchers also admit launching surveys that contain an option for customer comments with no intention of doing anything with the comments they receive.

Customer Comments Aren’t Necessary?

2 in 5 researchers it is sufficient to analyze only a small subset of my customers comments

Part of the problem—as the first bullet indicates—is that coding/analysis of responses to open-ended questions has historically been a time-consuming and labor-intensive process. (Happily, this is no longer the case.)

But a more troubling issue, it seems, is a widespread lack of recognition for the value of unstructured customer feedback, especially compared to quantitative survey data.

  • Almost half (41%) of researchers said actual voice-of-customer comments are of secondary importance to structured rating questions.
  • Of those who do read/analyze customer comments, 20% said it’s sufficient to just read/code a small subset of the comments rather than each and every

In short, we can conclude that many researchers omit or ignore customer comments because they believe they can get the same or better insights from quantitative ratings data.

This assumption is absolutely WRONG.

Misconception: Ratings Are Enough

I’ve posted on the serious problems with relying exclusively on quantitative data for insights before here.

But before I discovered text analytics, I used to be in the same camp as the researchers highlighted in our survey.

My first mistake was that I assumed I would always be able to frame the right questions and conceive of all possible relevant answers.

I also believed, naively, that respondents actually consider all questions equally and that the decimal point differences in mean ratings from (frequently onerous) attribute batteries are meaningful, especially if we can apply a T Test and the 0.23% difference is deemed “significant” (even if only at a directional 80% confidence level).

Since then, I have found time and time again that nothing predicts actual customer behavior better than the comment data from a well-crafted open-end.

For a real world example, I invite you to have a look at the work we did with Jiffy Lube.

There are real dollars attached to what our customers can tell us if we let them use their own words. If you’re not letting them speak, your opportunity cost is probably much higher than you realize.

Thank you for your readership,

I look forward to your COMMENTS!

@TomHCAnderson

[PS. Over 200 marketing researchers professionals completed the survey in just the first week in field (statistics above), and the survey is still fielding here. What I was most impressed with so far was ironically the quality and thought fullness of the two open ended comments that were provided. Thus I will be doing initial analysis and reporting here on the blog during the next few days. So come back soon to see part II and maybe even a part III of the analysis to this very short but interesting survey of research professionals]

Can Text Analytics Shed Light On Trump's Appeal?

From tacit to more explicit insights, text analytics helps answer the why’s in voting

Because of the interest in yesterday’s post I decided to continue on the topics of politics today.  As a marketing researcher and data scientist though I found yesterday’s analysis a bit more interesting. Not because of the findings per se, but because we were able to use text analytics to accurately predict real attitudes and behavior by not just ‘reading between the lines’ but extrapolating a relationship between seemingly non related attitudes and opinions, which of course are related and predictive when you look more closely.

Of course text analytics can be interesting when used on more explicit data as well. So today I’ll take a look at two more open ended comment questions two different surveys.

In case you're wondering, the benefit of a text answer rather than asking several structured survey questions with rating scales is that unaided text questions give a much truer measure of what issues are actually important to a respondent. Rating scale questions force respondents to have an opinion on issues even when there is none, and thus structured survey questions (even the popular ones like Net Promoter Score) are usually far less effective in predicting actual behavior than text data in our experience.

Reason for Political Affiliation

Immediately after the self-description exercise in yesterday’s analysis we obviously needed to ask what the respondents political affiliation was (so that we could understand what relationship, if any, there is between how we view ourselves and political affiliation).

Respondents were able to designate which party if any they were affiliated with, whether they considered themselves Independent, Tea Party, Green, or something else, and why?

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mm1

WhyDemRep

WhyDemRep

The ability to get a good quantitative relative measure to a why question is something unique to text analytics. Perhaps surprisingly there were rather few mentions of specific campaign issues. Instead the tendency was to use far more general reasons to explain why one votes a certain way.

While Republicans and Democrats are equally unlikely to mention “Conservatism’ and “Liberalism” when describing themselves (from yesterday's post), Republicans are about twice as likely to say they are affiliated with the Republican party because of their “Conservative” values (11% VS 5% “liberal” for Democrats).

Democrats say they vote the way they do because the Democratic party is “For the People”, “Cares about the Poor” and “the Middle [and] working class”.

Republicans on the other hand say they vote Republican because of “values” especially the belief that “you have to work for what you get”. Many also mention “God” and/or their “Christian” Faith as the reason. The desire for smaller/less government and greater Military/Defense spending are also significant reasons for Republicans.

Of course we could have probed deeper in the OE comments with a second question if we had wanted to. Still it is telling that specific issues like Healthcare, Education, Gay Rights and Taxes are less top-of-mind among voters than these more general attitudes about which party is right for them.

Describe Your Ideal President

As mentioned earlier we are looking toward social media to understand and build models. Therefore, we also recently asked a separate sample of n=1000 Americans, all who are active on Twitter, what qualities they felt the President of the United States (POTUS) should have.

mm44

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TextAnalyticsPOTUS

TextAnalyticsPOTUS

The chart above is divided by those who said they tend to vote or at least typically skew toward that respective party.

The findings do help explain the current political climate a bit. Both Democrats and Republicans were most likely to mention “honesty” as a quality they look for, perhaps indicating a greater frustration with politics in general. The idea of “honesty” though is more important to voters who skew toward the GOP.

Those who favor the Democratic party are significantly more likely to value traits like Intelligence, Compassion/Empathy, skill, educational attainment of the candidate and open-mindedness.

Those who lean Republican however are significantly more likely to value a candidate who is perceived both as a strong leader in general, but also more specifically is strongly for America. Rather than educational attainment, softer more tacit skills are valued by this group, for instance Republican voters put greater emphasis on experience and “know how”. Not surprisingly, based on yesterday’s data on how voters view themselves, Republican voters also value Family values and Christian faith in their ideal POTUS.

Research has shown that people prefer leaders similar to themselves. Looking back to some of the self descriptions in yesterday's data we definitely see a few similarities in the above...

Thanks for all the feedback on yesterday’s post. Please join me week after next when I plan on sharing some more interesting survey findings not related to politics, but of course to text analytics.

@TomHCAnderson

WhyVoteTextAnalytics

WhyVoteTextAnalytics

Tom H.C. Anderson

Tom H.C. Anderson

To learn more about how OdinText can help you understand what really matters to your customers and predict actual behavior,  please contact us or request a Free Software Demo here >

[NOTE: Tom H. C. Anderson is Founder of Next Generation Text Analytics software firm OdinText Inc. Click here for more Text Analytics Tips]

Text Analysis Predicts Your Politics Without Asking

How What You Say Says Way More Than What You Said

Pretend for a moment that you had a pen pal overseas and they asked you to describe yourself. What would you tell them? What makes you “you”?

It turns out that which traits, characteristics and aspects of your identity you choose to focus on may say more than you realize.

For instance, they can be used to predict whether you are a Democrat or a Republican.

With the U.S. presidential race underway in earnest, I thought it would be interesting to explore what if any patterns in the way people describe themselves could be used to identify their political affiliation.

So we posed the question above verbatim to a nationally representative sample of just over n=1000 (sourced via CriticalMix) and ran the responses through OdinText.

Not surprisingly, responses to this open-ended question were as varied as the people who provided them, but OdinText was nevertheless able to identify several striking and statistically significant differences between the way Republicans and Democrats described themselves.

NOT About Demographics

Let me emphasize that this exercise had nothing to do with demographics. We’re all aware of the statistical demographic differences between Republicans and Democrats.

For our purposes, what if any specific demographic information people shared in describing themselves was only pertinent to the extent that it constituted a broader response pattern that could predict political affiliation.

For example, we found that Republicans were significantly more likely than Democrats to say they have blonde hair.

Of course, this does not necessarily mean that someone with blonde hair is significantly more likely to be a Republican; rather, it simply means that if you have blonde hair, you are significantly more likely to feel it noteworthy to mention it when describing yourself if you are a Republican than if you are a Democrat.

Predicting Politics with Text Analytics

Predicting Politics with Text Analytics

Self-Image: Significant Differences

OdinText’s analysis turned up several predictors predictors for party affiliation, here are 15 examples indexed below.

  • Republicans were far more likely to include their marital status, religion, ethnicity and education level in describing themselves, and to mention that they are charitable/generous.

  • Democrats, on the other hand, were significantly more likely to describe themselves in terms of friendships, work ethic and the quality of their smile.

Interestingly, we turned up quite a few more predictors for Republicans than Democrats, suggesting that the former may be more homogeneous in terms of which aspects of their identities matter most. This translates to a somewhat higher level of confidence in predicting affinity with the Republican Party.

As an example, if you describe yourself as both “Christian” and “married,” without knowing anything else about you I can assume with 90% accuracy that you vote Republican.

Again, this does not mean that Christians who are married are more than 90% likely to be Republicans, but it does mean that people who mention these two things when asked to tell a stranger about themselves are extremely likely to be Republicans.

So What?

While this exercise was exploratory and the results should not be taken as such, it demonstrates that text analytics make it entirely possible to read between the lines and determine far more about you than one would think possible.

Obviously, there is a simpler, more direct way to determine a respondent’s political affiliation: just ask them. We did. That’s how we were able to run this analysis. But it’s hardly the point.

The point is we don’t necessarily have to ask.

In fact, we’ve already built predictive models around social media profiles and Twitter feeds that eliminate the need to pose questions—demographic, or more importantly, psychographic.

Could a political campaign put this capability to work segmenting likely voters and targeting messages? Absolutely.

But the application obviously extends well beyond politics. With an exponentially-increasing flood of Customer Experience FeedbackCRM and consumer-generated text online, marketers could predicatively model all manner of behavior with important business implications.

One final thought relating to politics: What about Donald Trump, whose supporters it has been widely noted do not all fit neatly into the conventional Republican profile? It would be pretty easy to build a predictive model for them, too! And that could be useful given the widespread reports that a significant number of people who plan to vote for him are reluctant to say so.

Support OdinText - Make Data Science Accessible!

Take 7 Seconds to Support the OdinText Mission: Help Make Data Science Accessible! I’m excited to announce that OdinText will participate in the IIEX2016 Insight Innovation Competition!

The competition celebrates innovation in market research and provides a platform for young companies and startups to showcase truly novel products and services with the potential to transform the consumer insights field.

Marketing and research are becoming increasingly complex, and the skills needed to thrive in this environment have changed.

To that end, OdinText was designed to make advanced data analytics and data science accessible to marketers and researchers.

Help us in that mission. It only takes 7 seconds.

Please visit http://www.iicompetition.org/idea/view/387 and cast a ballot for OdinText!

You can view and/or vote for the other great companies here if you like.

Thank you for your consideration and support!

Tom

Tom H. C. Anderson Founder - OdinText Inc. www.odintext.com Info/Demo Request

ABOUT ODINTEXT OdinText is a patented SaaS (software-as-a-service) platform for natural language processing and advanced text analysis. Fortune 500 companies such as Disney and Coca-Cola use OdinText to mine insights from complex, unstructured text data. The technology is available through the venture-backed Stamford, CT firm of the same name founded by CEO Tom H. C. Anderson, a recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research. The company is the recipient of numerous awards for innovation from industry associations such as ESOMAR, CASRO, the ARF and the American Marketing Association. Anderson tweets under the handle @tomhcanderson.

 

Attensity Sells IP to InContact: What Does it Mean?

Goodbye to Attensity, an early text analytics pioneer Attensity

Fellow text analytics colleagues,

I wanted to direct your attention to some industry news that broke today. It appears that Attensity—an OdinText competitor and a long time name in the text analytics software space—is selling its IP assets to InContact, a provider of call center solutions. (Read about it here.)

For people who’ve been watching the text analytics sector, this probably comes as no big surprise. It is well known that Attensity has been in the throes of some financial difficulty for a while. And just last month they sold their European business to an investment consortium. (Read about it here.)

It is my guess that Attensity’s text analysis software will be bundled into InContact’s portfolio as a value add for call center customers—essentially using the former’s basic NLP for a voice-to-text application and then to code results.

Whether or not Attensity’s product continues to exist in a standalone capacity, too, or what this means for their existing customers remains to be seen.

I think this development is noteworthy in part because Attensity was one of the earliest players in the text analytics space. In fact, even Claraview—the company from which Clarabridge was later spun off in 2006—initially licensed Attensity’s technology, before developing their own very similar tool.

As I noted in a very recent blog post, Attensity and Clarabridge both adhere to a rules-based approach that requires costly and time-consuming customization.

At the risk of being self-serving, it seems to me that as the text analytics market continues to mature and buyers become better informed, we’ll see increasing demand for more flexible solutions that are faster to get up and running and easier to use with a better total cost of ownership.

That’s good news for OdinText, but as the situation with Attensity suggests it doesn’t bode well for competitors with eight figure debt selling dated approaches.

That said, it is an extremely exciting time for industry as a whole with adoption continuing to increase as more and more use cases now move to clients ‘must have’ lists.

@TomHCAnderson

 

[NOTE: Tom is Founder and CEO of OdinText Inc.. A long time champion of text mining, in 2005 he founded Anderson Analytics LLC, the first consumer insights/marketing research consultancy focused on text analytics. In 2015 he founded OdinText SaaS which take a new, Next Generation approach to text analytics. He is a frequent speaker and data science guest lecturer at university and research industry events.]

OdinText Wins American Marketing Association Lavidge Global Marketing Research Prize

OdinTextAnalyticsAwardAMA AMA Honors Cloud-Based Text Analytics Software Provider OdinText for Making Data Science Accessible to Marketers

OdinText Inc., developer of the Next Generation Text Analytics SaaS (software-as-a-service) platform of the same name, today was named winner of the American Marketing Association’s  2016 Robert J. Lavidge Global Marketing Research Prize for innovation in the field.

The Lavidge Prize, which includes a $5000 cash award, globally recognizes a marketing research/consumer insight procedure or solution that has been successfully implemented and has a practical application for marketers.

According to Chris Chapman, President of the AMA Marketing Insights Council, OdinText earned the award for its contribution to advancing the practice of marketing by making data science accessible to non-data scientists.

“Consumers are creating oceans of unstructured text data, but putting this tremendously valuable information to practical use has posed a significant challenge for marketers and companies,” said Chapman.

“The nominations for OdinText highlighted how the company has distilled very complex applied analytics processes into an intuitive tool that enables marketers to run sophisticated predictive analyses and simulations by themselves, quickly and easily. This is exactly the kind of practical advancement we look for in awarding the Lavidge Prize,” added Chapman

The cloud-based OdinText software platform enables marketers with no advanced training or data science expertise to harness vast quantities of complex, unstructured text data—survey open-ends, call center transcripts, email, social media, discussion boards—and to rapidly mine valuable insights that would not have been otherwise obtainable without a data scientist.

“Marketing is evolving, getting both broader and deeper in terms of skill sets needed to succeed,” said FreshDirect Vice President of Business Intelligence and Analytics Jim DeMarco, who nominated OdinText for the Lavidge Prize.

“OdinText provides marketers with the capability to access more advanced analysis faster and helps the business they work on gain an information advantage. This is exactly the kind of innovation our industry needs right now,” DeMarco said.

The Lavidge Prize was presented in a special ceremony today at the AMA’s 2016 Analytics with Purpose Conference in Scottsdale, AZ. OdinText CEO Tom H. C. Anderson—a recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research—accepted the award on behalf of the firm.

“One of our goals in creating OdinText was to build the tool from an analyst’s perspective, not a software developer’s, so that a marketer armed with OdinText could derive the same insights but faster than a data scientist using traditional techniques and tools,” said Anderson.

“To be recognized for this achievement by the AMA—one of the largest and most prestigious professional associations for marketers in the world, which has devoted itself to leading the way forward into a new era of marketing excellence—is deeply gratifying,” said Anderson.

 

ABOUT ODINTEXT

OdinText is a patented SaaS (software-as-a-service) platform for natural language processing and advanced text analysis. Fortune 500 companies such as Disney and Shell Oil use OdinText to mine insights from complex, unstructured text data easily and rapidly. The technology is available through the venture-backed Stamford, CT firm of the same name founded by CEO Tom H. C. Anderson, a recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research. He tweets under the handle @tomhcanderson.

For more information, visit OdinText Info Request

ABOUT THE AMERICAN MARKETING ASSOCIATION

With a global network of over 30,000 members, the American Marketing Association (AMA) serves as one of the largest marketing associations in the world.  The AMA is the leading professional association for marketers and academics involved in the practice, teaching, and study of marketing worldwide.  Members of the AMA count on the association to be their most credible marketing resource, helping them to establish valuable professional connections and stay relevant in the industry with knowledge, training, and tools to enhance lifelong learning.

For more information, visit www.ama.org

Text analysis answers: Is the Quran really more violent than the Bible? (3of3)

Text analysis answers: Is the Quran really more violent than the Bible?by Tom H. C. Anderson

Text Analytics Bible Q

Part III: The Verdict

To recap…

President Obama in his State of the Union last week urged Congress and Americans to “reject any politics that target people because of race or religion”—clearly a rebuke of presidential candidate Donald Trump’s call for a ban on Muslims entering the United States.

This exchange, if you will, reflects a deeper and more controversial debate that has wended its way into not only mainstream politics but the national discourse: Is there something inherently and uniquely violent about Islam as a religion?

It’s an unpleasant discussion at best; nonetheless, it is occurring in living rooms, coffee shops, places of worship and academic institutions across the country and elsewhere in the world.

Academics of many stripes have interrogated the texts of the great religions and no doubt we’ll see more such endeavors in the service of one side or the other in this debate moving forward.

We thought it would be an interesting exercise to subject the primary books of these religions—arguably the core of their philosophy and tenets—to comparison using the advanced data mining technology that Fortune 500 corporations, government agencies and other institutions routinely use to comb through large sets of unstructured text to identify patterns and uncover insights.

So, we’ve conducted a surface-level comparative analysis of the Quran and the Old and New Testaments using OdinText to uncover with as little bias as possible the extent to which any of these texts is qualitatively and/or quantitatively distinct from the others using metrics associated with violence, love and so on.

Again, some qualifiers…

First, I want to make very clear that we have not set out to prove or disprove that Islam is more violent than other religions.

Moreover, we realize that the Old and New Testaments and the Quran are neither the only literature in Islam, Christianity and Judaism, nor do they constitute the sum of these religions’ teachings and protocols.

I must also reemphasize that this analysis is superficial and the findings are by no means intended to be conclusive. Ours is a 30,000-ft, cursory view of three texts: the Quran and the Old and New Testaments, respectively.

Lastly, we recognize that this is a deeply sensitive topic and hope that no one is offended by this exercise.

 

Analysis Step: Similarities and Dissimilarities

Author’s note: For more details about the data sources and methodology, please see Part I of this series.

In Part II of the series, I shared the results of our initial text analysis for sentiment—positive and negative—and then broke that down further across eight primary human emotion categories: Joy, Anticipation, Anger, Disgust, Sadness, Surprise, Fear/Anxiety and Trust.

The analysis determined that of the three texts, the Old Testament was the “angriest,” which obviously does not appear to support an argument that the Quran is an especially violent text relative to the others.

The next step was to, again, staying at a very high level, look at the terms frequently mentioned in the texts to see what if anything these three texts share and where they differ.

Similarity Plot

Text Analytics Similarity Plot 2

This is yet another iterative way to explore the data from a Bottom-Up data-driven approach and identify key areas for more in-depth text analysis.

For instance—and not surprisingly—“Jesus” is the most unique and frequently mentioned term in the New Testament, and when he is mentioned, he is mentioned positively (color coding represents sentiment).

“Jesus” is also mentioned a few times in the Quran, and, for obvious reasons, not mentioned at all in the Old Testament. But when “Jesus” is mentioned in the New Testament, terms that are more common in the Old Testament—such as “God” and “Lord”—often appear with his name; therefore the placement of “Jesus” on the map above, though definitely most closely associated with the New Testament, is still more closely related to the Old Testament than the Quran because these terms appear more often in the former.

Similarly, it may be surprising to some that “Israel” is mentioned more often in the Quran than the New Testament, and so the Quran and the Old Testament are more textually similar in this respect.

So…Is the Quran really more violent than the Old and New Testaments?

Old Testament is Most Violent

A look into the verbatim text suggests that the content in the Quran is not more violent than its Judeo-Christian counterparts. In fact, of the three texts, the content in the Old Testament appears to be the most violent.

Killing and destruction are referenced slightly more often in the New Testament than in the Quran (2.8% vs. 2.1%), but the Old Testament clearly leads—more than twice that of the Quran—in mentions of destruction and killing (5.3%).

New Testament Highest in ‘Love’, Quran Highest in ‘Mercy’

The concept of ‘Love’ is more often mentioned in the New Testament (3.0%) than either the Old Testament (1.9%) or the Quran (1.26%).

But the concept of ‘Forgiveness/Grace’ actually occurs more often in the Quran (6.3%) than the New Testament (2.9%) or the Old Testament (0.7%). This is partly because references to “Allah” in the Quran are frequently accompanied by “The Merciful.” Some might dismiss this as a tag or title, but we believe it’s meaningful because mercy was chosen above other attributes like “Almighty” that are arguably more closely associated with deities.

Text Analytics Plot 3

‘Belief/ Faith’, ‘Non-Members’ and ‘Enemies’

A key difference emerged immediately among the three texts around the concept of ‘Faith/Belief’.

Here the Quran leads with references to ‘believing’ (7.6%), followed by the New Testament (4.8%) and the Old Testament a distant third (0.2%).

Taken a step further, OdinText uncovered what appears to be a significant difference with regard to the extent to which the texts distinguish between ‘members’ and ‘non-members’.

Both the Old and New Testaments use the term “gentile” to signify those who are not Jewish, but the Quran is somewhat distinct in referencing the concept of the ‘Unbeliever’ (e.g.,“disbelievers,” “disbelieve,” “unbeliever,” “rejectors,” etc.).

And in two instances, the ‘Unbeliever’ is mentioned together with the term “enemy”:

“And when you journey in the earth, there is no blame on you if you shorten the prayer, if you fear that those who disbelieve will give you trouble. Surely the disbelievers are an open enemy to you

 An-Nisa 4:101

“If they overcome you, they will be your enemies, and will stretch forth their hands and their tongues towards you with evil, and they desire that you may disbelieve

Al-Mumtahina 60:2

That said, the concept of “Enemies” actually appears most often in the Old Testament (1.8%).

And while the concept of “Enemies” occurs more often in the Quran than in the New Testament (0.7% vs 0.5%, respectively), there is extremely little difference in how they are discussed (i.e., who and how to deal with them) with one exception: the Quran is slightly more likely than the New Testament to mention “the Devil” or “evil” as being an enemy (.2% vs 0.1%).

Conclusion

While A LOT MORE can be done with text analytics than what we’ve accomplished here, it appears safe to conclude that some commonly-held assumptions about and perceptions of these texts may not necessarily hold true.

Those who have not read or are not fairly familiar with the content of all three texts may be surprised to learn that no, the Quran is not really more violent than its Judeo-Christian counterparts.

Personally, I’ll admit that I was a bit surprised that the concept of ‘Mercy’ was most prevalent in the Quran; I expected that the New Testament would rank highest there, as it did in the concept of ‘Love’.

Overall, the three texts rated similarly in terms of positive and negative sentiment, as well, but from an emotional read, the Quran and the New Testament also appear more similar to one another than either of them is to the significantly “angrier” Old Testament.

Of course, we’ve only scratched the surface here. A deep analysis of unstructured data of this complexity requires contextual knowledge, and, of course, some higher level judgment and interpretation.

That being said, I think this exercise demonstrates how advanced text analytics and data mining technology may be applied to answer questions or make inquiries objectively and consistently outside of the sphere of conventional business intelligence for which our clients rely on OdinText.

I hope you found this project as interesting as I did and I welcome your thoughts.

Yours fondly,

Tom @OdinText

TOM DEC 300X250

 

Text analysis answers: Is the Quran really more violent than the Bible? (Part 2 of 3)

BIBLE 728x90 Text analysis answers: Is the Quran really more violent than the Bible? (Part 2 of 3) by Tom H. C. Anderson

Part II: Emotional Analysis Reveals Bible is “Angriest”

In my previous post, I discussed our potentially hazardous plan to perform a comparative analysis using an advanced data mining platform—OdinText—across three of the most important texts in human history: The Old Testament, The New Testament and the Quran.

Author’s note: For more details about the data sources and methodology, please see Part I of this series.

The project was inspired by the ongoing public debate around whether or not terrorism connected with Islamic fundamentalism reflects something inherently and distinctly violent about Islam compared to other major religions.

Before sharing the first set of results with you here today, due to the sensitive nature of this topic, I feel obliged to reiterate that this analysis represents only a cursory, superficial view of just the texts, themselves. It is in no way intended to advance any agenda or to conclusively prove anyone’s point.

Step 1: Sentiment Analysis

We started with a high-level look at Sentiment—positive and negative—and overall results were fairly similar: approx. 30% positive and 20% negative sentiment for each of the three texts. The Old Testament looked to have slightly more negative sentiment than either the New Testament or the Quran, but let’s come back to that later in more detail…

Staying at a high level, I was curious to see what the longitudinal pattern looked like across each of the three texts. Looking for any positive emotion in the texts from beginning to end allows us to get a sense how they progress longitudinally. (See figure 1)

Author’s note: Unlike the Old and New Testaments, in the Quran, verses (suras) are arranged in order of length and not in chronological order.

Any Positive Sentiment

Sentiment Analysis 1

Sentiment Analysis 2

Sentiment Analysis 3

While there is some fluctuation throughout each in terms of positive sentiment, the New Testament appears to be unique in that it peaks on positive sentiment (Corinthians) and ends on a less positive note (Revelations).

It’s also worth noting that positive and negative sentiment are usually highly correlated. In other words when there is more emotion in text, usually, though not always, there is both more positive and negative sentiment.

But let’s look deeper into emotions, beyond simple positive vs. negative sentiment (which is rarely very interesting) and into the eight major human emotion categories: Joy, Anticipation, Anger, Disgust, Sadness, Surprise, Fear/Anxiety and Trust.

Author’s note: These eight major emotion categories were derived from widely-accepted theory in modern psychology.

Step 2: Emotional Analysis

A look at the combined Old and New Testaments—the Bible—compared to the Quran reveals similarities and differences. The Bible and Quran are fairly uniform in ‘Surprise’, ‘Sadness’ and ‘Disgust’. But the Bible registers higher in ‘Anger’ and the Quran rates higher in ‘Joy’ but also in ‘Fear/Anxiety’ and ‘Trust’.

Sentiment Analysis Bible Quran

As we mentioned yesterday, we decided to split the Old and New Testaments for analysis for a couple of reasons. Here’s what they look like:

Sentiment Analysis 5

Comparing our three religious texts across the eight major emotions we find that the Old Testament is the ‘Angriest’ (including most mentions of ‘Disgust’); it also contains the least amount of ‘Joy’.

Here’s an example of a passage that registered under ‘Anger’:

But the LORD said to him "Not so; if anyone kills Cain he will suffer vengeance seven times over." Then the LORD put a mark on Cain so that no one who found him would kill him.

Genesis 4:15

In text analytics, ‘Disgust’ rarely appears outside of food categories; however, it appears in Leviticus several times:

…whether among all the swarming things or among all the other living creatures in the water—you are to detest.

And since you are to detest them, you must not eat their meat and you must detest their carcasses.

Anything living in the water that does not have fins and scales is to be detestable to you.

'These are the birds you are to detest and not eat because they are detestable: the eagle the vulture the black vulture

Leviticus 11:10-13 The Quran, on the other hand, contains the most ‘Fear/Anxiety’ and ‘Trust/Belief’ issues. In this case ‘Fear/Anxiety’ is highly linked to ‘Trust’. Terms such as “doubt” and “disbelief” appear repeatedly in the Quran and are relevant to and affect both of these two primary emotions.

Or like abundant rain from the cloud in which is darkness, and thunder and lightning; they put their fingers into their ears because of the thunder-peal, for fear of death. And Allah encompasses the disbelievers.

Quaran Sūrat al-Baqarah 2:19 As noted in figure 2 above, the New Testament has relatively more ‘Anticipation’ and ‘Surprise’:

But if we hope for what we do not yet have we wait for it patiently.

Romans 8:25

Everyone was amazed and gave praise to God. They were filled with awe and said, ‘We have seen remarkable things today.” 

Luke 5:26

Tomorrow in Part 3, we’ll take a deeper dive to understand some of the underlying reasons for these differences in greater detail and we’ll look into which, if any, of these texts is significantly more violent. Stay tuned!

Up Next: Part III – Violence, Mercy and Non-Believers