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Most of this is done automatically, and you won't even notice it's happening. However, it's important to understand that automatic text analysis makes use of a number of natural language processing techniques NLP like the below.
Tokenization is the process of breaking up a string of characters into semantically meaningful parts that can be analyzed e. The examples below show two different ways in which one could tokenize the string 'Analyzing text is not that hard'.
Incorrect : Analyzing text is not that hard. Correct : Analyzing text is not that hard. Once the tokens have been recognized, it's time to categorize them. Part-of-speech tagging refers to the process of assigning a grammatical category, such as noun, verb, etc. With all the categorized tokens and a language model i. This process is known as parsing. In other words, parsing refers to the process of determining the syntactic structure of a text.
To do this, the parsing algorithm makes use of a grammar of the language the text has been written in. Different representations will result from the parsing of the same text with different grammars. The examples below show the dependency and constituency representations of the sentence 'Analyzing text is not that hard'.
Dependency grammars can be defined as grammars that establish directed relations between the words of sentences. Dependency parsing is the process of using a dependency grammar to determine the syntactic structure of a sentence:. Constituency phrase structure grammars model syntactic structures by making use of abstract nodes associated to words and other abstract categories depending on the type of grammar and undirected relations between them.
Constituency parsing refers to the process of using a constituency grammar to determine the syntactic structure of a sentence:. As you can see in the images above, the output of the parsing algorithms contains a great deal of information which can help you understand the syntactic and some of the semantic complexity of the text you intend to analyze.
Depending on the problem at hand, you might want to try different parsing strategies and techniques. However, at present, dependency parsing seems to outperform other approaches. Stemming and lemmatization both refer to the process of removing all of the affixes i.
The main difference between these two processes is that stemming is usually based on rules that trim word beginnings and endings and sometimes lead to somewhat weird results , whereas lemmatization makes use of dictionaries and a much more complex morphological analysis. The differences in the output have been boldfaced:. To provide a more accurate automated analysis of the text, we need to remove the words that provide very little semantic information or no meaning at all.
These words are also known as stopwords: a, and, or, the, etc. There are many different lists of stopwords for every language. However, it's important to understand that you might need to add words to or remove words from those lists depending on the texts you want to analyze and the analyses you would like to perform. You might want to do some kind of lexical analysis of the domain your texts come from in order to determine the words that should be added to the stopwords list.
Well, the analysis of unstructured text is not straightforward. There are countless text analysis methods, but two of the main techniques are text classification and text extraction. Text classification also known as text categorization or text tagging refers to the process of assigning tags to texts based on its content.
In the past, text classification was done manually, which was time-consuming, inefficient, and inaccurate. But automated machine learning text analysis models often work in just seconds with unsurpassed accuracy. The most popular text classification tasks include sentiment analysis i.
In text classification, a rule is essentially a human-made association between a linguistic pattern that can be found in a text and a tag. Rules usually consist of references to morphological, lexical, or syntactic patterns, but they can also contain references to other components of language, such as semantics or phonology.
Here's an example of a simple rule for classifying product descriptions according to the type of product described in the text:. The most obvious advantage of rule-based systems is that they are easily understandable by humans. However, creating complex rule-based systems takes a lot of time and a good deal of knowledge of both linguistics and the topics being dealt with in the texts the system is supposed to analyze.
On top of that, rule-based systems are difficult to scale and maintain because adding new rules or modifying the existing ones requires a lot of analysis and testing of the impact of these changes on the results of the predictions. Machine learning-based systems can make predictions based on what they learn from past observations. These systems need to be fed multiple examples of texts and the expected predictions tags for each.
This is called training data. The more consistent and accurate your training data, the better ultimate predictions will be. When you train a machine learning-based classifier, training data has to be transformed into something a machine can understand, that is, vectors i.
By using vectors, the system can extract relevant features pieces of information which will help it learn from the existing data and make predictions about the texts to come.
There are a number of ways to do this, but one of the most frequently used is called bag of words vectorization. You can learn more about vectorization here. Once the texts have been transformed into vectors, they are fed into a machine learning algorithm together with their expected output to create a classification model that can choose what features best represent the texts and make predictions about unseen texts:.
The trained model will transform unseen text into a vector, extract its relevant features, and make a prediction:. There are many machine learning algorithms used in text classification. The Naive Bayes family of algorithms is based on Bayes's Theorem and the conditional probabilities of occurrence of the words of a sample text within the words of a set of texts that belong to a given tag.
Vectors that represent texts encode information about how likely it is for the words in the text to occur in the texts of a given tag. With this information, the probability of a text's belonging to any given tag in the model can be computed.
Once all of the probabilities have been computed for an input text, the classification model will return the tag with the highest probability as the output for that input. Support Vector Machines SVM is an algorithm that can divide a vector space of tagged texts into two subspaces: one space that contains most of the vectors that belong to a given tag and another subspace that contains most of the vectors that do not belong to that one tag.
Classification models that use SVM at their core will transform texts into vectors and will determine what side of the boundary that divides the vector space for a given tag those vectors belong to. Based on where they land, the model will know if they belong to a given tag or not. The most important advantage of using SVM is that results are usually better than those obtained with Naive Bayes.
However, more computational resources are needed for SVM. These algorithms use huge amounts of training data millions of examples to generate semantically rich representations of texts which can then be fed into machine learning-based models of different kinds that will make much more accurate predictions than traditional machine learning models:.
Hybrid systems usually contain machine learning-based systems at their cores and rule-based systems to improve the predictions. Classifier performance is usually evaluated through standard metrics used in the machine learning field: accuracy , precision , recall , and F1 score.
Understanding what they mean will give you a clearer idea of how good your classifiers are at analyzing your texts. It is also important to understand that evaluation can be performed over a fixed testing set i.
Accuracy is the number of correct predictions the classifier has made divided by the total number of predictions. In general, accuracy alone is not a good indicator of performance. For example, when categories are imbalanced, that is, when there is one category that contains many more examples than all of the others, predicting all texts as belonging to that category will return high accuracy levels.
This is known as the accuracy paradox. To get a better idea of the performance of a classifier, you might want to consider precision and recall instead. Precision states how many texts were predicted correctly out of the ones that were predicted as belonging to a given tag. In other words, precision takes the number of texts that were correctly predicted as positive for a given tag and divides it by the number of texts that were predicted correctly and incorrectly as belonging to the tag.
We have to bear in mind that precision only gives information about the cases where the classifier predicts that the text belongs to a given tag. This might be particularly important, for example, if you would like to generate automated responses for user messages.
In this case, before you send an automated response you want to know for sure you will be sending the right response, right? In other words, if your classifier says the user message belongs to a certain type of message, you would like the classifier to make the right guess.
This means you would like a high precision for that type of message. Recall states how many texts were predicted correctly out of the ones that should have been predicted as belonging to a given tag. In other words, recall takes the number of texts that were correctly predicted as positive for a given tag and divides it by the number of texts that were either predicted correctly as belonging to the tag or that were incorrectly predicted as not belonging to the tag.
Recall might prove useful when routing support tickets to the appropriate team, for example. In this case, making a prediction will help perform the initial routing and solve most of these critical issues ASAP. If the prediction is incorrect, the ticket will get rerouted by a member of the team.
When processing thousands of tickets per week, high recall with good levels of precision as well, of course can save support teams a good deal of time and enable them to solve critical issues faster. The F1 score is the harmonic means of precision and recall. It tells you how well your classifier performs if equal importance is given to precision and recall.
In general, F1 score is a much better indicator of classifier performance than accuracy is. Cross-validation is quite frequently used to evaluate the performance of text classifiers. The method is simple. First of all, the training dataset is randomly split into a number of equal-length subsets e. Next, all the performance metrics are computed i. Finally, the process is repeated with a new testing fold until all the folds have been used for testing purposes. Once all folds have been used, the average performance metrics are computed and the evaluation process is finished.
Text Extraction refers to the process of recognizing structured pieces of information from unstructured text. For example, it can be useful to automatically detect the most relevant keywords from a piece of text, identify names of companies in a news article, detect lessors and lessees in a financial contract, or identify prices on product descriptions.
Regular Expressions a. In this case, a regular expression defines a pattern of characters that will be associated with a tag. For example, the pattern below will detect most email addresses in a text if they preceded and followed by spaces:. Verify Didn't receive the code? Contact Support. By signing up, you agree to our Terms and Conditions.
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Jump to your favorite action. Lotus Milf. Vote on categories x. Pornstars: Suggest. Thank you for your suggestions! Can I generate a NPS for different parts of the business? Then do one for the company as a whole? Is it running data? So if I report back on a monthly basis, if someone gave us a 2 in February is that still counted in the results in September?
Does that make sense? Or do I start from a clean slate? For Q1: If your respondent base is large enough you can calculate NPS for different parts of your business. For Q2: Again if your respondent base is large enough you can track month by month for the short term follow-up, and on long term you can use year by year NPS scores.
Thanks for the great article. My thoughts are that NPS could be though of as a scale between and 10 being likely to warn people away but then concentrated into a smaller 11 point scale for ease of use. Do you think this is a fair description of why the top end categories are much smaller? Olie, This is an original argumentation, but the easier explanation according to me is that a promotor is someone who is willing to convinve others to use your product or service. We are a B2B company that has many users from a company using our service.
So when multiple users from a company respond to a survey, we average their rating and then use that to calculate our overall NPS. Like five users from a company might have scored us a 9,9,7,6,5, so the average for that company is 7 and we drop that company from our calculations.
Do you think this is a right way to calculate NPS, or should we use the entire user responses for our calculation? I would not drop an entire comany based on an average score.
An average can hide a lot of heterogenious scores e. My advice is to use the individual scores. Nice article.. Is it better to use no may be yes for the question, instead of number scale between Sathya, the NPS question is a worldwide standardized question type, with use of the number scale. For benchmark purposed I advise to stick with the defined methodology. Good afternoon We have a business that is consultation based, ie not e commerce.
When is the best time to catch them ie via email or text. We are after a good response rate. Do you have any data to support that text or email is better. Thank Lucy. There is no conclusive time to send your emails. The best tip is to consider your audience in your email marketing. Optimal launch times are also subjective to the device the recipient is using. Desktop and smartphone users are most active between 3 and 4pm and are more likely to open emails during business hours.
Tablet users are most active from 8 to 9pm and are more likely to open emails outside business hours. The most active hours overall are from 2 to 5pm. Start doing some simple email and text split tests and see which times and which medium your recipients respond to best. The NPS in your example is 16 Dear Gert, Thanks for such an informative article. They are the most vulnerable of the entire set, and hence there should be some mechanism to grade or rank them and some level of analysis could be done to arrive at a more refined NPS.
They are not included in the actual calculation of your NPS, but nevertheless they can have a major influence. The easiest way to increase your NPS score, is to turn your passives into promoters. This group just needs that little bit of extra service and effort to make them genuine promoters of your company or brand. The NPS is based on flawed mathematics and flawed questioning. On the other hand this simplicity is also the strength of the NPS score, the one number gives you a first indication of your companies performance and as it is widely spread between industries you have lots of benchmarks.
An evolution over time of the NPS for your company in my opinion still is a very usefull indicator of the direction your company is going. I have some questions. Is helpful to explain to the interviewee what is being detractor, passive and promoter?
What are the number ranges? I would certainly not explain the interviewee which score makes them a detractor, passive or promoter, as this will influence the NPS score and make this not comparable to other NPS scores. If you want to know the drivers for the score, you can add an open question following the NPS question where you as for a motivation of the given score. My current company does that, but in some other companies I have seen agent level CSAT being calculated as an average score of all their surveys.
Many large companies do use NPS as a major or the only indication of customer satisfaction scores for their agents, others can of course calculate the CSAT based on a combination of questions, inclusing NPS or not.
P: Promoter I have one more question. Would this be a fair assessment? I would be very careful about giving your own interpretation to the answer scales and changing the intervals. Just stick to the standard calculation and track the NPS scores over time to see what the trend is. Great article, If we have been using the standard 10 pt Likert scale without zero for likelihood to recommend, do you think that our NPS results will be problematic?
From a trending perspective, I believe it is the same result — movement in the metric will be the same as if the scale included 0. As you have less detractor answer categories, you should expect the NPS score to increase. But in this case the impact this will only be marginal if any at all. Someone who wants to give 0 score will now give a 1 score, which will make no difference in the end for the calculation. It is however important to continue with the same scale for the trending.
Thanks for answering questions about NPS! I am wondering about errors with the Likert Scale. Could this be a mistake? Hi, Very nice article explaning NPS in detail.. I just have two questions: 1 What is the minimum percentage of samples required to be taken for an organisation of customers to get a valid response 2 Does every transaction response NPS transform to the final relationship NPS? I would go for a minimum of samples, but I would repeat this on a monthly basis to track the evolution over time.
On yearly basis you would then have a very solid base of responses. Can you elaborate on your 2nd question, this is not totally clear to me. We have just started to use the NPS within my organisation. If you would have to put them in a category I would call them passives. As this would have no impact on the calculation of the NPS score the best solution is to exclude them alltogether. In my opinion option d is the only valid statement you can make about the evolution of the NPS. I was wondering when the best time is to ask the NPS question for a services site.
Do you ask on the site directly, or afterwards via email? Why is one preferable over the other? I would do this on the site with a pop-up appearing immediately when your customer leaves the site. There is an error in your article.
Absolute numbers are always positive, which makes the following quote an oxymoron, since it cant be negative. Thanks for the remark. We use the expression abolute number in this case to make a distinction with percentage, not in the mathematical sense of absolute value of a number.
The term absolute number is also used in forums and blogs on Satmetrix website developers of the NPS. Also, the formula you show always creates a percentage.
The way the formula is described is wrong. Mathematically speaking you are absolutely correct, the difference of 2 percentages is also a percentage. In some publications the NPS-score is given as a percentage, in others as an absolute index. And on their website they state: Subtracting the percentage of Detractors from the percentage of Promoters yields the Net Promoter Score, which can range from a low of if every customer is a Detractor to a high of if every customer is a Promoter.
Regardless whether it is a percentage or not. I had my data sorted incorrectly over time. I bet the early returns are just statistical noise cause by the small sample.
I doubt detractors are distributed as early respondents unless you had a bunch of referral traffic early in the survey from a site with a higher propensity to refer detractors to you. This seems especially true given that—contra your earlier assertion—the scores increased to match previous scores as the sample increased. This is an actively engaged site. The first hour, the results, largely matched previous surveys for the same site. The 2nd hour and a half, the score systematically eroded.
My theory is that on a site with high engagement. Then, over time, I now believe that the more casual users are contributing — users who are not as enthusiastic or heavy users, and therefore reflect a less optimistic view of the site. I recently did a survey with 39 respondents during a week. And i noticed the behaviour that you are referring to and came to the same conclusion.
Across my surveying, I have had larger and smaller sample sizes, based on my clients leaving the survey links up longer and shorter times. I got curious about the impact of sample sizes, and wondering what the sweet spot was for sample size.
What surprised me was that after levelling off after several hundred data points, the score then gradually erodes systematically over time in many cases. I would have thought that the score should stabilize further, but in fact, it just slides down. Has anyone else looked at rolling NPS over the course of an individual data set? A lot depends on how you ask and where. Yes I would be happy to have a small bonus on the nps and hopefully I could help to build a good nps for the company but as engineers we will be forced to ask customer to lie and give us good nps to protect our pay and if the customer likes us hopefully they will but this will then lead to a unrealistic nps of the company ,everyone is very angry with this new pay structure and we now feel like it does not matter if we do a ok job and keep the customer happy or if we do an exceptional job as our work quality does not have much impact now its down to nps.
Nps should be used but not abused. I can understand your employer when he wants to get the whole team involved in improving the NPS and company performance. But obviously you only have a minor impact on this score, so I agree with you that the impact on your salary also should be marginally. Ok so how do people score is then when they only have detractors or could they score with passives as well. Thanks for your help its much appreciated. If everyone on the survey were Passives, the final score would be 0 which would also be true if for every Promoter there was one Detractor.
If you start out with a negative NPS, Passives will move you closer to 0, but they only way to get positive is to have Promoters. Example: 2 Promoters, 6 Detractors, 2 Passives.
If your total survey size was 20 and all twenty came back with 10 more passives, your final score would be. Still higher than the negative NPS you would get if those 10 new responses were all Detractors instead of Passives!
I understand how to calculate. Just puzzled that passives are not included yet they are in a way as if you have 10 survey and 6 give you a 9 or 10 and 2 give you 7 or 8 passive then the remaining 2 give you your score is Technically they affect your score so how are the passive and not included.
To be passive and have no affect those 2 survey would have to be removed completely? Sylvia, I would recommend to keep the period between the customer interaction and NPS question or any other survey related to the interaction as short as possible. Ideally you would have a continuous monitoring system keeping track of NPS on a monthly or even day-to-day basis. Hello Gert, Thanks for your reply. For the period between the customer interaction and the NPS question, it makes sense when it relates to a transactional NPS to keep the interaction period as short as possible.
But would this still apply for measuring relationship or brand NPS? So just wondering if that is the best practice not using a filter for the interaction period e. Thank you,. I do not know your particular situation, but in any case I would still recommend at least one interaction in the last 6 montht as a minimum prerequisite to have a valid NPS score.
What is the standard pratice for this question? Should we ensure that our customers have had an interaction with us in the past 90 days to measure our NPS? Thank you! Is it possible to make an equation that works for this?
My company uses NPS on an individual level. Is there a known way to do that without plugging in extra surveys until my number hits the 54 result? If you have one, I would greatly appreciate it. My math is flawed somewhere.
Thanks so much! The NPS can be a result of an endless number of combinations of promotors and detractors, and also your denominator is variable taking into account the passives. In my opinion it is not possible to put this in a simple excel equation.
But if anyone else out there should have a solution for this, feel free to share your thoughts with us! It is possible to calculate this if you know all of the data, or at least the NPS and the sample size. Of course, each Passive or Detractor makes it harder to hit the Goal. Having a sample size twice as big will make it twice as hard to hit your goal starting at the same initial NPS. So it would take 24 consecutive Promoters to hit the goal if the initial sample was people and the NPS was still This is the type of stat that might be better to look at a monthly basis rather than a daily basis.
It looks like for every 3 new Detractors you need 10 or more new Promoters and for every 5 new Passives you need 6 new Promoters in order to get a NPS of Dear Gert, thanks for an interesting post! We have about active users on site, so would it be right to totally randomly get answers each month I suppose I should show survey to about users and next month do same thing but with another random users? Am I thinking right? Thank you very much for your answer!
What you suggest seems a logical way to proceed. Navy submarine officer and nuclear engineer. NASA has extolled space station missions in low-Earth orbit as critical training grounds and incubators for technologies that will help achieve the goals of a sustainable lunar presence and eventual human flights to Mars.
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