topic modeling and machine learning with LDA - machine-learning

I want to label some documents, I tried the LDA algorithm but the results were too messy. I decided to use a supervised approach, so I created my own topic-word matrix but I don't know how to generate a document-topic matrix. Do you know some good topic modeling algorithm that can be trained using topic-word matrix ?

If you do have a correct topic-word matrix created. You only need to compute the weights of topic for each documents. For example you could use the occurence of each word in each documents and then summing the topic weight of those words. You might need to add some coefficients like number of occurence but it is pretty straightforward.
You can also use LDA algorithm but ignoring the training step which is made to process the topic-word matrix. I do not know which implementation you use but following the one of Sklearn you can directly pass the matrix as components_ attributes and then use the transform function.

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Mutli-Class Text Classifcation (using TFIDF and SVM). How to implement a scenario where one feedback may belong to more than one class?

I have a file of raw feedbacks that needs to be labeled(categorized) and then work as the training input for SVM Classifier(or any classifier for that matter).
But the catch is, I'm not assigning whole feedback to a certain category. One feedback may belong to more than one category based on the topics it talks about (noun n-grams are extracted). So, I'm labeling the topics(terms) not the feedbacks(documents). And so, I've extracted the n-grams using TFIDF while saving their features so i could train my model on. The problem with that is, using tfidf, it returns a document-term matrix that's train_x, but on the other side, I've got train_y; The labels that are assigned to each n-gram (not the whole document). So, I've ended up with a document to frequency matrix that contains x number of rows(no of documents) against a label of y number of n-grams(no of unique topics extracted).
Below is a sample of what the data look like. Blue is the n-grams(extracted by TFIDF) while the red is the labels/categories (calculated for each n-gram with a function I've manually made).
Instead of putting code, this is my strategy in implementing my concept:
The problem lies in that part where TFIDF producesx_train = tf.Transform(feedbacks), which is a document-term matrix and it doesn't make sense for it to be an input for the classifier against y_train, which is the labels for the terms and not the documents. I've tried to transpose the matrix, it gave me an error. I've tried to input 1-D array that holds only feature values for the terms directly, which also gave me an error because the classifier expects from X to be in a (sample, feature) format. I'm using Sklearn's version of SVM and TfidfVectorizer.
Simply, I want to be able to use SVM classifier on a list of terms (n-grams) against a list of labels to train the model and then test new data (after cleaning and extracting its n-grams) for SVM to predict its labels.
The solution might be a very technical thing like using another classifier that expects a different format or not using TFIDF since it's document focused (referenced) or even broader, a whole change of approach and concept (if it's wrong).
I'd very much appreciate it if someone could help.

How to combine TFIDF features with other features

I have a classic NLP problem, I have to classify a news as fake or real.
I have created two sets of features:
A) Bigram Term Frequency-Inverse Document Frequency
B) Approximately 20 Features associated to each document obtained using pattern.en (https://www.clips.uantwerpen.be/pages/pattern-en) as subjectivity of the text, polarity, #stopwords, #verbs, #subject, relations grammaticals etc ...
Which is the best way to combine the TFIDF features with the other features for a single prediction?
Thanks a lot to everyone.
Not sure if your asking technically how to combine two objects in code or what to do theoretically after so I will try and answer both.
Technically your TFIDF is just a matrix where the rows are records and the columns are features. As such to combine you can append your new features as columns to the end of the matrix. Probably your matrix is a sparse matrix (from Scipy) if you did this with sklearn so you will have to make sure your new features are a sparse matrix as well (or make the other dense).
That gives you your training data, in terms of what to do with it then it is a little more tricky. Your features from a bigram frequency matrix will be sparse (im not talking data structures here I just mean that you will have a lot of 0s), and it will be binary. Whilst your other data is dense and continuous. This will run in most machine learning algorithms as is although the prediction will probably be dominated by the dense variables. However with a bit of feature engineering I have built several classifiers in the past using tree ensambles that take a combination of term-frequency variables enriched with some other more dense variables and give boosted results (for example a classifier that looks at twitter profiles and classifies them as companies or people). Usually I found better results when I could at least bin the dense variables into binary (or categorical and then hot encoded into binary) so that they didn't dominate.
What if you do use a classifier for the tfidf but use the pred to add a new feature say tfidf and the probabilities of it to give a better result, here is a pic from auto ml blueprint to show you the same The results were > 90 percent vs 80 percent for current vs the two separate classifier ones

Do all machine learning algorithm usage Word frequency as a feature?

I use scikit learn for classification. And mainly work with NAive bayes, SVM, Neural network. There are variant in each of them.
I see for training algo create vectors. What does this vector contains?
For all algorithm does it consider word frequency as a feature? If yes then how they differ?
For text classification you usually create a vector of words frequency, or tf-idf to be able to compute distances between two documents. You could use all kinds of method to create these weights on word.
The words (features) can be extracted by just a splitting the documents on separator but you can use more complex methods like stemming (keep only the root of the words).
You will find lots of example in the sklearn documentation. For instance :
http://scikit-learn.org/stable/auto_examples/text/document_classification_20newsgroups.html
This iPython Notebook could be a good start too.

Machine Learning, After training, how exactly does it get a prediction? opencv

So after you have a machine learning algorithm trained, with your layers, nodes, and weights, how exactly does it go about getting a prediction for an input vector? I am using MultiLayer Perceptron (neural networks).
From what I currently understand, you start with your input vector to be predicted. Then you send it to your hidden layer(s) where it adds your bias term to each data point, then adds the sum of the product of each data point and the weight for each node (found in training), then runs that through the same activation function used in training. Repeat for each hidden layer, then does the same for your output layer. Then each node in the output layer is your prediction(s).
Is this correct?
I got confused when using opencv to do this, because in the guide it says when you use the function predict:
If you are using the default cvANN_MLP::SIGMOID_SYM activation
function with the default parameter values fparam1=0 and fparam2=0
then the function used is y = 1.7159*tanh(2/3 * x), so the output
will range from [-1.7159, 1.7159], instead of [0,1].
However, when training it is also stated in the documentation that SIGMOID_SYM uses the activation function:
f(x)= beta*(1-e^{-alpha x})/(1+e^{-alpha x} )
Where alpha and beta are user defined variables.
So, I'm not quite sure what this means. Where does the tanh function come into play? Can anyone clear this up please? Thanks for the time!
The documentation where this is found is here:
reference to the tanh is under function descriptions predict.
reference to activation function is by the S looking graph in the top part of the page.
Since this is a general question, and not code specific, I did not post any code with it.
I would suggest that you read about appropriate algorithm that your are using or plan to use. To be honest there is no one definite algorithm to solve a problem but you can explore what features you got and what you need.
Regarding how an algorithm performs prediction is totally depended on the choice of algorithm. Support Vector Machine (SVM) performs prediction by fitting hyperplanes on the feature space and using some metric such as distance for learning and than the learnt model is used for prediction. KNN on the other than uses simple nearest neighbor measurement for prediction.
Please do more work on what exactly you need and read through the research papers to get proper understanding. There is not magic involved in prediction but rather mathematical formulations.

How to choose classifier on specific dataset

When given the dataset, normally m instances by n features matrix, how to choose the classifier that is most appropriate for the dataset.
This is just like what algorithm to solve a prime Number. Not every algorithm solve any problem means each problem assigned which finite no. of algorithm. In machine learning you can apply different algorithm on a type of problem.
If matrix contain real numbered features then you can use KNN algorithm can be used. Or if matrix have words as feature then you can use naive bayes classifier which is one of best for text classification. And Machine learning have tons of algorithm you can read them apply to your problem which fits best. Hope you understand what I said.
An interesting but much more general map I found:
http://scikit-learn.org/stable/tutorial/machine_learning_map/
If you have weka, you can use experimenter and choose different algorithms on same data set to evaluate different models.
This project compares many different classifiers on different typical datasets.
If you have no idea, you could use this simple tool auto-weka which will test all the different classifiers you selected within different constraints. Before using auto-weka, you may need to convert your data to ARFF using Weka or just manually (many tutorial on youtube).
The best classifier depends on your data (binary/string/real/tags, patterns, distribution...), what kind of output to predict (binary class / multi-class / evolving classes / a value from regression ?) and the expected performance (time, memory, accuracy). It would also depend on whether you want to update your model frequently or not (ie. if it is a stream, better use an online classifier).
Please note that the best classifier may not be one but an ensemble of different classifiers.

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