I am now working on a document recommendation program and I am kinda stuck here.
For each document, I have a score assigned according to user's actions. Then, when a new document comes in, I need to predict how user will like it and rerank the whole documents again according to their scores. My solution is to use a threshold to divide those scores into "recommend" and "not recommend". Then naiveBayes or other classification models can either give me a label or return the possibility of that label (I am using NLTK package to do text analytics).
Am I on the right way? My question is when I get that possibility, how can I convert it into the score that I use to do the ranking? Or I should use logistic regression in scikit instead?
Thanks!
It sounds like you are trying to force a ranking problem into a classification problem. What you really want to do is learn how to rank the documents given a "query".
I would suggest trying out something like the SVM-Rank algorithm. It takes as input a set of "recommended" and "not recommended" vectors and then learns how to rank them so that the recommended ones come first. There is also a simple python tool in dlib you can use to do it. See here for an example: http://dlib.net/svm_rank.py.html
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i been trying to learn a bit of machine learning for a project that I'm working in. At the moment I managed to classify text using SVM with sklearn and spacy having some good results, but i want to not only classify the text with svm, I also want it to be classified based on a list of keywords that I have. For example: If the sentence has the word fast or seconds I would like it to be classified as performance.
I'm really new to machine learning and I would really appreciate any advice.
I assume that you are already taking a portion of your data, classifying it manually and then using the result as your training data for the SVM algorithm.
If yes, then you could just append your list of keywords (features) and desired classifications (labels) to your training data. If you are not doing it already, I'd recommend using the SnowballStemmer on your training data features.
Im reusing word2vec for products on my website and users. I would like to say that a user is NEGATIVELY associated to a product if he has visited the page < 5 seconds and POSITIVELY if he spent > 30 seconds on the page. Is there a way to specify this in word2vec? Or is there some other tool that enables this?
Although your question is not well defined but I think you want to store the relation of user with the product which has nothing to do with word2vec. word2vec essentially gives you a mapping from strings to contiguous domain vectors. In your problem you should give a separate new feature of User-Product relationship (NEGATIVE or POSITIVE) along with the word2vec features and you can let the model retrain the word-embeddings according to this new POSITIVE/NEGATIVE feature while solving your particular task. This way the model will adjust the word-embeddings and get some of the desired effect of the POSITIVE/NEGATIVE features.
Please be more elaborate so that I can answer your question in a better way.
I want to predict my input price based on a list of questions/answers using azure machine learning.
I built one using the "bayesian linear regression" but it seems that it is predicting the price based on the prices i have in my dataset and not based on the Q/A.
Am i in the wrong path or am i missing something?
Any suggestion would be helpful.
Check the Q/A s that you using is not having missing values. If there's any missing values follow data preprocessing techniques to fill those.
What kind of answers do you have as inputs? (yes/no, numeric values, different textual answers, etc...) In my opinion numerical values and yes/no inputs makes your model more accurate.
Try different regression algorithms (https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-cheat-sheet/) and check their accuracy.
you need to set features and label properly. if you publish your experiment in Gallery using unlisted mode and paste the link here, we can take a look.
I am trying to do a timeline detection problem using text classification. As a newbie I am confused as to how I can go about with this. Is this a classification problem? i.e, Can I use the years(timelines) as outcomes and solve this as a classification problem?
You should be able to solve this as a classification problem as you suggest. An option could be to find or build a corpus consisting of texts tagged with the period in which they're set, and train a classification algorithm on this data set.
Another option could be to train a word space model on such a data set, and generate vectors for different periods of time (e.g. the 50s, 60s etc.). You could then create a document vector for the text you wish to classify, and find which of these time vectors yields the best match.
Might not work, but it could be interesting to see what results you get.
Hope this helps!
I am embarking upon a NLP project for sentiment analysis.
I have successfully installed NLTK for python (seems like a great piece of software for this). However,I am having trouble understanding how it can be used to accomplish my task.
Here is my task:
I start with one long piece of data (lets say several hundred tweets on the subject of the UK election from their webservice)
I would like to break this up into sentences (or info no longer than 100 or so chars) (I guess i can just do this in python??)
Then to search through all the sentences for specific instances within that sentence e.g. "David Cameron"
Then I would like to check for positive/negative sentiment in each sentence and count them accordingly
NB: I am not really worried too much about accuracy because my data sets are large and also not worried too much about sarcasm.
Here are the troubles I am having:
All the data sets I can find e.g. the corpus movie review data that comes with NLTK arent in webservice format. It looks like this has had some processing done already. As far as I can see the processing (by stanford) was done with WEKA. Is it not possible for NLTK to do all this on its own? Here all the data sets have already been organised into positive/negative already e.g. polarity dataset http://www.cs.cornell.edu/People/pabo/movie-review-data/ How is this done? (to organise the sentences by sentiment, is it definitely WEKA? or something else?)
I am not sure I understand why WEKA and NLTK would be used together. Seems like they do much the same thing. If im processing the data with WEKA first to find sentiment why would I need NLTK? Is it possible to explain why this might be necessary?
I have found a few scripts that get somewhat near this task, but all are using the same pre-processed data. Is it not possible to process this data myself to find sentiment in sentences rather than using the data samples given in the link?
Any help is much appreciated and will save me much hair!
Cheers Ke
The movie review data has already been marked by humans as being positive or negative (the person who made the review gave the movie a rating which is used to determine polarity). These gold standard labels allow you to train a classifier, which you could then use for other movie reviews. You could train a classifier in NLTK with that data, but applying the results to election tweets might be less accurate than randomly guessing positive or negative. Alternatively, you can go through and label a few thousand tweets yourself as positive or negative and use this as your training set.
For a description of using Naive Bayes for sentiment analysis with NLTK: http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/
Then in that code, instead of using the movie corpus, use your own data to calculate word counts (in the word_feats method).
Why dont you use WSD. Use Disambiguation tool to find senses. and use map polarity to the senses instead of word. In this case you will get a bit more accurate results as compared to word index polarity.