I am distributing a survey to students, and this survey has multiple-choice questions. What I want is, when the answers are revealed depending on the answers, I want the pc to send automatically an individualised email that tells the student what to do.
Is there a way of doing this?
Like for example, if I asked a student these two questions:
1) Have you ever studied trigonometric functions
a) Yes, I am pretty good in them
b) Yes, but I need some resources to review
c) No, never bothered.
2) Have you ever studied Optimization Techniques?
a) Yes, I am pretty good in them
b) Yes, but I need some resources to review
c) No
If a student answers, was 1) a, and 2) c
Then an email is generated.
" Thank you so much for completing the survey, based on your result, and it seems that you need to study Optimization techniques. The resources that are required are: bla bla bla. "
You can do things like this using many different mailinglist software packages and cloud services. This question is a bit too general and open-ended for a specific answer, so here's a general one.
For example, the open-source Sympa mailing list server ( www.sympa.org ) is capable of having metadata (survey results) for individual list members, and then using its built-in mailmerge features to construct personalised messages that use the metadata to enable or disable sections. However, it would mean installing a mailing list server which may be a bit of overkill if this is a one-off. You can also do this sort of thing with MailChimp (though I am loathe to recommend them).
Of course, whichever solution you use, you will need to upload the survey results and email addresses to your mailmerge database.
The exact details of how to do this are left as an exercise for the reader, though this should provide some ideas :)
Related
My Question
What are the best practices for creating a customized report based on a user form input? Specifically, how do I create an easy to maintain system which takes user input which is collected in a form and generate multiple paragraphs that explains the results of analysis.
Background
I am working on a very large multiyear project with a startup (who is my client). My job is to program analysis and generate reports to users. The pipeline for data looks like this:
Users enter information into a form -> results are calculated based on user input -> reports are displayed to users that share analysis.
It is really important to my client that some of the analysis results are displayed in paragraphs in a non-formal user friendly tone. The challenge is that the form and analysis are quite complex and will only get more complex over time. An example of the type of template for the paragraphs looks something like this:
resultsParagraphText=`Hi ${userName}. We found that the best ice cream flavour for you is ${bestIceCreamFlavor}. These other flavors ${otherFlavors} might be good for you. Here are the reasons why you might enjoy these flavors: ${reasonsWhyGoodFlavors}.
However we would not recommend these other flavors ${badFlavors}. Here are the reasons you should avoid this bad flavors: ${reasonsWhyBadFlavors}.`
These results paragraphs, of which there of many, have several minor problems which combined are significant:
If there is a bug in the code, minor visual errors would be visible to end users (capitalization errors, missing/extra commas, and so on).
A lot of string comparisons (e.g. if answers.previousFlavors.includes("Vanilla")) are required to generate the results paragraphs. Minor errors in the forms (e.g. vanilla in the form is not capitalized so answers.previousFlavors.includes("Vanilla") returns false even when user enters vanilla.) can cause errors in the results paragraph.
Changes in different parts of the project (form, analysis) directly effect how the results paragraph is made. Bad types, differences in string values, null or undefined values not being caught directly have an impact on how the results paragraph is made.
There are many edge cases (e.g. What if the user has no other suitable good flavors for them? The the sentence These other flavors ${otherFlavors} might be good for you. needs to be excluded).
It is hard to write paragraphs that use templates and have a non-formal tone.
and so on.
I have charts and other types of ways to display results and have explained to the client the challenges of sharing the information in paragraph form.
What I am looking for
I need examples, how tos, best practices on how to build a maintainable system for generating customized paragraphs based on user input. I know how to solve each of the individual issues (as they are fairly simple) but in a large project this will become very hard to maintain.
Notes
I have no clue what tags to use for the post. Feel free to edit/add tags if you know more appropriate ones.
The project is planning to use machine learning in the future other parts of the project. If there is a ML/AI solution that is useful please tell me.
I am working primarily in JavaScript, Python, C, and R, but if there is a library or tool in any other language please tell me. Finding a solution is very important to me and I would be willing to learn a lot find a best solution.
To avoid this question being removed because I have rephrased it to avoid asking for personal opinion, instead asking for existing examples or how tos. I can also imagine that others might find a solution fairly useful. If you can edit it to make the question less subjective please do so.
If you have any questions or need clarification feel free to ask. Any help is appreciated.
I have 20,000 messages (combination of email and live chat) between my customer and my support staff. I also have a knowledge base for my product.
Often times, the questions customers ask are quite simple and my support staff simply point them to the right knowledge base article.
What I would like to do, in order to save my support staff time, is to show my staff a list of articles that may likely be relevant based on the initial user's support request. This way they can just copy and paste the link to the help article instead of loading up the knowledge base and searching for the article manually.
I'm wondering what solutions I should investigate.
My current line of thinking is to run analysis on existing data and use a text classification approach:
For each message, see if there is a response with a link to a how-to article
If Yes, extract key phrases (microsoft cognitive services)
TF-IDF?
Treat each how-to as a 'classification' that belongs to sets of key phrases
Use some supervised machine learning, support vector machines maybe to predict which 'classification, aka how-to article' belongs to key phrase determined from a new support ticket.
Feed new responses back into the set to make the system smarter.
Not sure if I'm over complicating things. Any advice on how this is done would be appreciated.
PS: naive approach of just dumping 'key phrases' into search query of our knowledge base yielded poor results since the content of the help article is often different than how a person phrases their question in an email or live chat.
A simple classifier along the lines of a "spam" classifier might work, except that each FAQ would be a feature as opposed to a single feature classifier of spam, not-spam.
Most spam-classifiers start-off with a dictionary of words/phrases. You already have a start on this with your naive approach. However, unlike your approach a spam classifier does much more than a text search. Essentially, in a spam classifier, each word in the customer's email is given a weight and the sum of weights indicates if the message is spam or not-spam. Now, extend this to as many features as FAQs. That is, features like: FAQ1 or not-FAQ1, FAQ2 or not-FAQ2, etc.
Since your support people can easily identify which of the FAQs an e-mail requires then using a supervised learning algorithm would be appropriate. To reduce the impact of any miss-classification errors, then consider the application presenting a support person with the customer's email followed by the computer generated response and all the support person would have to-do is approve the response or modify it. Modifying a response should result in a new entry in the training set.
Support Vector Machines are one method to implement machine learning. However, you are probably suggesting this solution way too early in the process of first identifying the problem and then getting a simple method to work, as well as possible, before using more sophisticated methods. After all, if a multi-feature spam classifier works why invest more time and money in something else that also works?
Finally, depending on your system this is something I would like to work-on.
Don't know where to start on this one so hopefully you guys can clear up my question. I have project where email will be searched for specific words/patterns and stored in a structured manner. Something that is done with Trip it.
The article states that they developed a DataMapper
The DataMapper is responsible for taking inbound email messages
addressed to plans [at] tripit.com and transforming them from the
semi-structured format you see in your mail reader into a highly
structured XML document.
There is a comment that also states
If you're looking to build this yourself, reading a little bit about
Wrappers and Wrapper Induction might be helpful
I Googled and read about wrapper induction but it was just too broad of a definition and didn't help me understand how one would go about solving such problem.
Is there some open source project out there that does similar things?
There are a couple of different ways and things you can do to accomplish this.
The first part, which involves getting access to the email content I'll not answer here. Basically, I'll assume that you have access to the text of emails, and if you don't there are some libraries that allow you to connect java to an email box like camel (http://camel.apache.org/mail.html).
So now you've got the email so then what?
A handy thing that could help is that lingpipe (http://alias-i.com/lingpipe/) has an entity recognizer that you can populate with your own terms. Specifically, look at some of their extraction tutorials and their dictionary extractor (http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html) So inside of the lingpipe dictionary extractor (http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html) you'd simply import the terms you're interested in and use that to associate labels with an email.
You might also find the following question helpful: Dictionary-Based Named Entity Recognition with zero edit distance: LingPipe, Lucene or what?
Really a very broad question, but I can try to give you some general ideas, which might be enough to get started. Basically, it sounds like you're talking about an elaborate parsing problem - scanning through the text and looking to apply meaning to specific chunks. Depending on what exactly you're looking for, you might get some good mileage out of a few regular expressions to start - things like phone numbers, email addresses, and dates have fairly standard structures that should be matchable. Other data points might benefit from some indicator words - the phrase "departing from" might indicate that what follows is an address. The natural language processing community also has a large tool set available for text processing - check out things like parts of speech taggers and semantic analyzers if they're appropriate to what you're trying to do.
Armed with those techniques, you can follow a basic iterative development process: For each data point in your expected output structure, define some simple rules for how to capture it. Then, run the application over a batch of test data and see which samples didn't capture that datum. Look at the samples and revise your rules to catch those samples. Repeat until the extractor reaches an acceptable level of accuracy.
Depending on the specifics of your problem, there may be machine learning techniques that can automate much of that process for you.
What if I choose to use GMail's awesome mail archive search capabilities on my database? What if, for every transaction that my database is responsible, I emailed details of that transaction to a GMail address that exists for the sole purpose of searching and retrieving transactions.
Anyone logged into that account could search according to labels, invoice numbers, customer names - whatever using Google's search engine. The results are presented as 'email messages'.
Imagine a user working from the standard (web-based) GMail account searches for an invoice number via GMail's search box - he's returned all instances where the db did anything that included that unique number. Opening any of these 'email messages' would have the static text text included at the time of the transactions (historical and tracking gold) but could also carry a Gadget that could transform the 'message' into an editor so as to execute a new transaction on that invoice.
Imagine further that I wasn't the first one to think of this - cuz surely i'm not - and even if i were, i'm not smart enough to execute the idea alone.
Are you aware of efforts similar to this?
thx
[?belongs on superuser instead?]
An interesting idea, however given your search parameters it might be unreliable. Although gmail's search is great, I have found issues when searching for partial terms. Case in point, I had an email whose subject line was "stuffas". When I searched for "stuffa" I got no results, when I searched for "stuffas" I got the email in the search result. Additionally, I had an email with an 8 digit number inside the body. When I searched for 7 digits out of 8, I got no results, but when I put all 8 digits, the email appeared in the results. So, search in gmail may not be as powerful of a solution as you think. Again this is my experience, I'd love to hear if someone is able to partial search numbers in gmail.
I just had the same idea; 4 years after you. It still doesn't look like this has 'been done before' in any production sense. But now in 2014, I really don't see why not. Python packages for interfacing with gmail are already there and dead-simple to use. It does not take a whole lot of abstraction to turn this into a generalized key-value storage.
Its probably not exactly the fastest database, and not the best solution for everything; but as an easy-to-use, easy to search, trivial to configure, 100% uptime, cloud stored and backed up, free-as-in-beer database, its pretty epic as far as I can see.
Anyone else has seen examples of this having been done before?
Edit: having thought about it some more, there are several answers as to why this is a bad idea:
gmail does not permit random access from different locations; it will block you account. quite a showstopper
amazon simpleDB also gives you a simple key-value store with the same characteristics (plus good python support), and isn't THAT big of a pain to set up if you are willing to spend a day wrapping your head around it. And is also effectively free for the kind of traffic that youd be able to cram into a gmail account.
I want to analyze answers to a web survey (Git User's Survey 2008 if one is interested). Some of the questions were free-form questions, like "How did you hear about Git?". With more than 3,000 replies analyzing those replies entirely by hand is out of the question (especially that there is quite a bit of free-form questions in this survey).
How can I group those replies (probably based on the key words used in response) into categories at least semi-automatically (i.e. program can ask for confirmation), and later how to tabularize (count number of entries in each category) those free-form replies (answers)? One answer can belong to more than one category, although for simplicity one can assume that categories are orthogonal / exclusive.
What I'd like to know is at least keyword to search for, or an algorithm (a method) to use. I would prefer solutions in Perl (or C).
Possible solution No 1. (partial): Bayesian categorization
(added 2009-05-21)
One solution I thought about would be to use something like algorithm (and mathematical method behind it) for Bayesian spam filtering, only instead of one or two categories ("spam" and "ham") there would be more; and categories itself would be created adaptively / interactively.
Text::Ngrams + Algorithm::Cluster
Generate some vector representation for each answer (e.g. word count) using Text::Ngrams.
Cluster the vectors using Algorithm::Cluster to determine the groupings and also the keywords which correspond to the groups.
You are not going to like this. But: If you do a survey and you include lots of free-form questions, you better be prepared to categorize them manually. If that is out of the question, why did you have those questions in the first place?
I've brute forced stuff like this in the past with quite large corpuses. Lingua::EN::Tagger, Lingua::Stem::En. Also the Net::Calais API is (unfortunately, as Thomposon Reuters are not exactly open source friendly) pretty useful for extracting named entities from text. Of course once you've cleaned up the raw data with this stuff, the actual data munging is up to you. I'd be inclined to suspect that frequency counts and a bit of mechanical turk cross-validation of the output would be sufficient for your needs.
Look for common words as keywords, but through away meaningless ones like "the", "a", etc. After that you get into natural language stuff that is beyond me.
It just dawned on me that the perfect solution for this is AAI (Artificial Artificial Intelligence). Use Amazon's Mechanical Turk. The Perl bindings are Net::Amazon::MechanicalTurk. At one penny per reply with a decent overlap (say three humans per reply) that would come to about $90 USD.