I'd like to know what are the most recurrent in a given text or group of text (pulled from a database) in ruby.
Does anyone know what are the best practices?
You might start with statistical natural language processing. Also, you may be able to leverage one or more of the libraries mentioned on the AI Ruby Plugins page.
Related
I have users that have authenticated with a social media site. Now based on their last X (let's say 200) posts, I want to map how much that content matches up with a finite list of keywords.
What would be the best way to do this to capture associated words/concepts (maybe that's too difficult) or just get a score of how much, say, my tweet history maps to 'Walrus' or 'banana'?
Would a naive Bayes work here to separate into 'matches' and 'no match'?
In Python I would say NLTK can easily do it. In Ruby maybe gem called lda-ruby will help you. Whole LDA concept is well explained here - look at Sarah Palin's email for example. There's even the example of an app (not entirely in Ruby, but still) which did that -> github.com/echen/sarah-palin-lda
Or maybe I just say stupid things and that can't help you at all. I'm not an expert ;)
A simple bayes would work in this case, it is highly used to detect if emails are spam or not so for a simple keyword matching it should work pretty well.
For this problem you could also apply a recommendation system where you look for the top recommended keyword for a user (or for a post).
There are a ton of ways for doing this. I would recommend you to read Programming Collective Intelligence. It is explained using python but since you know ruby there should be not problem to understand the code.
I have a huge amount of documents (mainly pdfs and doc's) I want to classify, so I can search over them according to certain tags. These tags could either be of my own (I put the tags to the document) or extracted from the text.
I've just seen a post related to this (Classify data using Apache Mahout), but perhaps there is something even more simple.
Mahout might be overkill for your problem - but you can get a fairly quick, easy solution by using OpenNLP.
http://opennlp.sourceforge.net/api/index.html
Specifically, look at the opennlp.tools.doccat package. Essentially, you have to go through and manually tag a small(ish) set of the items for each category you desire. If they are really distinct, you can get away with a small sample size.
You can use the DocumentCategorizerME.train() static function to train a collection of documents, where each requires a category tag and the text block to train on. Then, you can initialize the DocumentCategorizerME with the trained model and begin classifying all the rest of your documents.
Once you do this, you can (I think) write the model to a file so you don't have to ever do that again.
This post on extracting keywords and classifying webpages is related and may be helpful. In your example it sounds like you can use tags in lieu of the keyword extraction piece (although you may want to use both in combination). Weka is easy to use, I would definitely recommend giving it a look.
I'm building a site that allows users to make sense of a debate by graphically representing arguments for and against a particular issue. (Wrangl)
I'd like to categorise these debates so they are more easily found and connected. I don't want to irritate the person creating the debate by asking them to add tags and categories before they see any benefit, so I'm looking at a way of automatically extracting keywords.
What's a good approach for taking the debate's title and description (and possibly the content of the arguments themselves once there are some) to pull out, say, ten strong keywords that could be used as metadata to connect similar debates together, or even as the content of the "meta" keywords tag in the head of the HTML page where the debate is viewable. Eg. Datamapper vs ActiveRecord
The site is coded in Ruby with Sinatra, using DataMapper for data storage. I'm ideally looking for something which will work on Heroku (I don't have a way of writing files to disk dynamically), and I'd consider a web service, an API or ideally a Ruby gem.
Maybe you can use TextAnalyzer.
I understand that you're wanting to find an easy way of achieving this, I've recently dived into the world of NLP (Natural Language Processing) and Text-mining and its a daunting process of which most went far above my head.
Although i managed to code some functionality that resembles what you're looking for, though I did it in PHP. What i would suggest, that if you want it tailored to your project (Wrangl) then do it yourself.
Using the Porter stemming algorithm which I'm sure there will be Ruby code for.
Ruby Porter stemmer
You can try the salsaAPI to automatically extract keywords and categorize the debates!
I'm looking for a library or technique to detect the input language of blocks of text provided by users. Online lookups (like Google translate) won't work for this task as I'm writing an app which must run offline.
Thanks.
Here are two more n-gram-based gems you might want to try. They work offline.
https://github.com/echen/unsupervised-language-identification, optimized for separating english and other languages (has a live demo)
https://github.com/feedbackmine/language_detector, less specialized, will detect more languages. Some languages may need some extra training — I found it to be not precise enough for German text.
For anyone interested, I've found http://rubygems.org/gems/kenwaln-whatlanguage, which is performing excellently.
I'm using CLD which I really like, succinct and easy to use. Give it a try.
A quick demo of WhatLanguage in Ruby:
http://www.youtube.com/watch?v=lNqZ2cqOReo&list=UUJ_3fstMOH-g4yBxtvgAWkw&index=0&feature=plcp
I would like to represent data that gives an overview but allows them to drill down in an inline fashion - so if you had a grouping of say 6 objects the user could expand the data and it would show the 6 objects immeadiately below it before any more high level data.
It would appear that MSHFlexgrid gives this ability but I can't find any information about actually using it, or what it's limitations are (can you have differing number of fields and/or can they have different spacing, what about column headers, indentation at for the start, etc).
I found this site, but the images are broken (in ie8 and ff3.5). Google searches show people just using the flat data representation but nothing using the hierarchical properties). Does anyone know any good tutorials or forums with a good discussion about pitfalls?
Due to lack of information about using it, I am thinking of coding my own version but if anyone has done work in this area I haven't found it - I would of thought it would be a natural wish for data representation. If someone has coded a version of this (any language) then I wouldn't mind reading about it - maybe my idea of how to do it wouldn't be the best way.
You might want to check out vbAccelerator. He has a Multi-Column Treeview control that sounds like what you may be looking for. He gives you the source and has some pretty decent samples.
The MSHFlexGrid reference pages and the "using the MSHFlexGrid" topic in the Visual Basic manual?
Sorry if you've already looked at these!