I'm looking for some advice in the problem of classifying users into various groups based on there answers to a sign up process.
The idea is that these classifications will group people with similar travel habits, i.e. adventurous, relaxing, foodie etc. This shouldn't be a classification known to the user, so isn't as simple as just asking what sort of holidays they like ( The point is to remove user bias/not really knowing where to place yourself).
The way I see it working is asking questions such as apps they use, accounts they interact with on social media (gopro, restaurants etc) , giving some scenarios and asking which sounds best, these would be chosen from a set provided to them, hence we have control over the variables. The main problem I have is how to get numerical values associated to each of these.
I've looked into various Machine learning algorithms and have realised this is most likely a clustering problem but I cant seem to figure out how to use this style of question to assign a value to each dimension that will actually give a useful categorisation.
Another question I have is whether there is some resources where I could find information on the sort of questions to ask users to gain information that'd allow classification like this.
The sort of process I envision is one similar to https://www.thread.com/signup/introduction if anyone is familiar with it.
Any advice welcomed.
The problem you have at hand is that you want to calculate a similarity measure based on categorical variables, which is the choice of their apps, accounts etc. Unless you measure the similarity of these apps with respect to an attribute such as how foodie is the app, it would be a hard problem to specify. Also, you would need to know all the possible states a categorical variable can assume to create a similarity measure like this.
If the final objective is to recommend something that similar people (based on app selection or social media account selection) have liked or enjoyed, you should look into collaborative filtering.
If your feature space is well defined and static (known apps, known accounts, limited set with few missing values) then look into content based recommendation systems, something as simple as Market Basket Analysis can give you a reasonable working model.
Else if you really want to model the system with a bunch of features that can assume random states, this could be done with multivariate probabilistic models, if the structure (relationships and influences between features) is well defined, you could benefit from Probabilistic Graphical Models, such as Bayesian Networks.
You really do need to define your problem better before you start solving it though.
You can use prime numbers. If each choice on the list of all possible choices is assigned a different prime, and the user's selection is saved as a product, then you will always know if the user has made a particular choice if the modulo of selection/choice is 0. Beauty of prime numbers, voila!
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I used machine learning to train depression related sentences. And it was LinearSVC that performed best. In addition to LinearSVC, I experimented with MultinomialNB and LogisticRegression, and I chose the model with the highest accuracy among the three. By the way, what I want to do is to be able to think in advance which model will fit, like ml_map provided by Scikit-learn. Where can I get this information? I searched a few papers, but couldn't find anything that contained more detailed information other than that SVM was suitable for text classification. How do I study to get prior knowledge like this ml_map?
How do I study to get prior knowledge like this ml_map?
Try to work with different example datasets on different data types by using different algorithms. There are hundreds to be explored. Once you get the good grasp of how they work, it will become more clear. And do not forget to try googling something like advantages of algorithm X, it helps a lot.
And here are my thoughts, I think I used to ask such questions before and I hope it can help if you are struggling: The more you work on different Machine Learning models for a specific problem, you will soon realize that data and feature engineering play the more important parts than the algorithms themselves. The road map provided by scikit-learn gives you a good view of what group of algorithms to use to deal with certain types of data and that is a good start. The boundaries between them, however, are rather subtle. In other words, one problem can be solved by different approaches depending on how you organize and engineer your data.
To sum it up, in order to achieve a good out-of-sample (i.e., good generalization) performance while solving a problem, it is mandatory to look at the training/testing process with different setting combinations and be mindful with your data (for example, answer this question: does it cover most samples in terms of distribution in the wild or just a portion of it?)
In the regression problem I'm working with, there are five independent columns and one dependent column. I cannot share the data set details directly due to privacy, but one of the independent variables is an ID field which is unique for each example.
I feel like I should not be using ID field in estimating the dependent variable. But this is just a gut feeling. I have no strong reason to do this.
What shall I do? Is there any way I decide which variables to use and which to ignore?
Well, I agree with #desertnaut. Id attribute does not seem relevant when creating a model and provides no help in prediction.
The term you are looking for is feature selection. Since it's a comprehensive section so I would just tell you the methods that are mostly used by data scientists.
As for regression problems you can try correlation heatmap to find the features that are highly correlated with the target.
sns.heatmap(df.corr())
There are several other ways too like PCA,using tree inbuilt feature selection methods to find the right features for your model.
You can also try James Phillips method. This approach is limited since model time complexity will increase linearly with the features. But in your case where you've only four features to compare you can try it out. You can compare the regression model trained with all the four features with the model trained with only three features by dropping one of the four features recursively. This would mean training four regression models and comparing them.
According to you, the ID variable is unique for each example. So the model won't be able to learn anything from this variable as with every example, you get a new ID and hence no general patterns to learn as every ID only occurs once.
Regarding feature elimination, it depends. If you have domain knowledge, based on that alone you can engineer/ remove features as needed. If you don't know much about the domain, you can try out some basic techniques like Backward Selection, Forward Selection, etc via Cross Validation to get the model with the best value of the metric that you're working with.
I’m working on web app where users can ask questions. These questions should be categorized by some criteria based on question content, title, user data, region and so on. Next these questions should be processed in so way: for some additional information requests should be sent, others should be deleted or marked as spam and some – sent directly to some specialist.
The problem is that users can’t choose the right category themselves, it’s pretty complex things and users can cheat.
Are there any approaches how to do that automatically? For now a few persons do this job filtering questions. Perhaps some already done solutions exist.
This is a really complex task. You should take a look at supervised machine learning classification algorithms. You can try to use similar to some spam filtering algorithm (https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering)
Gather some number of questions categorized before (labeled examples).
Gather some number of words (vocabulary) used for questions classifications (identify group).
Process question text removing “stop words” and replace words with their stems.
Map question text, title, user data and so to some numbers (question vector).
Use some algorithm like SVM to create and use classifier (model)
But it’s like very general approach you can look at. It’s hard to say something more specific without additional details. I don’t think you can find already done solution, it’s pretty specific task. But of cause you can use a lot of machine-learning frameworks.
My question is related to the project I've just started working on, and it's a ChatBot.
The bot I want to build has a pretty simple task. It has to automatize the process of purchasing movie tickets. This is pretty close domain and the bot has all the required access to the cinema database. Of course it is okay for the bot to answer like “I don’t know” if user message is not related to the process of ordering movie tickets.
I already created a simple demo just to show it to a few people and see if they are interested in such a product. The demo uses simple DFA approach and some easy text matching with stemming. I hacked it in a day and it turned out that users were impressed that they are able to successfully order tickets they want. (The demo uses a connection to the cinema database to provide users all needed information to order tickets they desire).
My current goal is to create the next version, a more advanced one, especially in terms of Natural Language Understanding. For example, the demo version asks users to provide only one information in a single message, and doesn’t recognize if they provided more relevant information (movie title and time for example). I read that an useful technique here is called "Frame and slot semantics", and it seems to be promising, but I haven’t found any details about how to use this approach.
Moreover, I don’t know which approach is the best for improving Natural Language Understanding. For the most part, I consider:
Using “standard” NLP techniques in order to understand user messages better. For example, synonym databases, spelling correction, part of speech tags, train some statistical based classifiers to capture similarities and other relations between words (or between the whole sentences if it’s possible?) etc.
Use AIML to model the conversation flow. I’m not sure if it’s a good idea to use AIML in such a closed domain. I’ve never used it, so that’s the reason I’m asking.
Use a more “modern” approach and use neural networks to train a classifier for user messages classification. It might, however, require a lot of labeled data
Any other method I don’t know about?
Which approach is the most suitable for my goal?
Do you know where I can find more resources about how does “Frame and slot semantics” work in details? I'm referring to this PDF from Stanford when talking about frame and slot approach.
The question is pretty broad, but here are some thoughts and practical advice, based on experience with NLP and text-based machine learning in similar problem domains.
I'm assuming that although this is a "more advanced" version of your chatbot, the scope of work which can feasibly go into it is quite limited. In my opinion this is a very important factor as different methods widely differ in the amount and type of manual effort needed to make them work, and state-of-the-art techniques might be largely out of reach here.
Generally the two main approaches to consider would be rule-based and statistical. The first is traditionally more focused around pattern matching, and in the setting you describe (limited effort can be invested), would involve manually dealing with rules and/or patterns. An example for this approach would be using a closed- (but large) set of templates to match against user input (e.g. using regular expressions). This approach often has a "glass ceiling" in terms of performance, but can lead to pretty good results relatively quickly.
The statistical approach is more about giving some ML algorithm a bunch of data and letting it extract regularities from it, focusing the manual effort in collecting and labeling a good training set. In my opinion, in order to get "good enough" results the amount of data you'll need might be prohibitively large, unless you can come up with a way to easily collect large amounts of at least partially labeled data.
Practically I would suggest considering a hybrid approach here. Use some ML-based statistical general tools to extract information from user input, then apply manually built rules/ templates. For instance, you could use Google's Parsey McParseface to do syntactic parsing, then apply some rule engine on the results, e.g. match the verb against a list of possible actions like "buy", use the extracted grammatical relationships to find candidates for movie names, etc. This should get you to pretty good results quickly, as the strength of the syntactic parser would allow "understanding" even elaborate and potentially confusing sentences.
I would also suggest postponing some of the elements you think about doing, like spell-correction, and even stemming and synonyms DB - since the problem is relatively closed, you'll probably have better ROI from investing in a rule/template-framework and manual rule creation. This advice also applies to explicit modeling of conversation flow.
So I have a data set which consists of tweets from various news organizations. I've loaded it into RapidMiner, tokenized it, and produced some n-grams of it. Now I want to be able to have RapidMiner automatically classify my data into various categories based on the topic of the tweets.
I'm pretty sure RapidMiner can do this, but according to the research I've done into it, I need a training data set to be able to show RapidMiner how I want things classified. So I need a training data set, though given the categories I wanted to classify things into, I might have to create my own.
So my questions are these:
1) Is there a training data set for twitter data that focuses more on the topic of the tweet as opposed to a sentiment analysis publicly available?
2) If there isn't one publicly available, how can I create my own? My idea to do it was to go through the tweets themselves and associate the tokens and n-grams with the categories I want. Some concerns I have with that are that I won't be able to manually classify enough tweets to create a training data set comprehensive enough so that I can get a good accuracy rate for the automatic classifier.
3) Any general advice for topical classification of text data would be great. This is the first time that I've done a project like this, and I'm sure there are things I could improve on. :)
There may be training corpora that work for you, but you need to say what your topic or categories are to identify it. The fact that this is Twitter may be relevant, but the data source is likely to be much less relevant to the classification accuracy you will achieve than the topic is. So if you take the infamous 20 newsgroups data set this is likely to work on Twitter as well, but only if the categories you are after are the 20 categories from that data set. If you want to classify cats vs dogs or Android vs iPhone you need to find a data set for that.
In most cases you will have to create initial labels manually, which is, as you say, a lot of work. One workaround might be to start with something simpler like a keyword search to create subsets of your tweets for which you know they deal with a particular category. Then you create the model on top of that and hope that it generalizes to identify the same categories even though the original keywords do not occur.
Alternatively, depending on your application (and if you actually want to build an applicaion), you may as well start with only a small data set and accept that you have poor classification. Then you generate classifications, show them to the users of your apps, and collect some form of explicit or implicit feedback on the classification (e.g. users can flag tweets as incorrectly classified). This way you improve your training corpus and periodically update your model.
Finally, if you do not know what your topics are and you want RapidMiner to identify the topics, you may want to try clustering as opposed to classification. Just create a few clusters and look at the top words for each cluster. They may well be quite dissimilar and describe what the respective clusters are about.
I believe your third question may be a bit broad for stackoverflow and is probably better answered by a text book.