Comments for StreamHacker

Comment on Execnet vs Disco for Distributed NLTK by Hadoop MapReduce

Hadoop MapReduce — Thu, 19 Apr 2012 12:53:00 +0000

Nice Post dude.. Keep it up.

Comment on Part of Speech Tagging with NLTK Part 1 – Ngram Taggers by VasilisG

VasilisG — Sun, 08 Apr 2012 19:55:00 +0000

Hi Jacob, when I try to create the ubt I receive the following error:

Traceback (most recent call last):
File “”, line 1, in
    ubt_tagger = backoff_tagger(train_sents, [nltk.tag.UnigramTagger, nltk.tag.BigramTagger, nltk.tag.TrigramTagger])
File “”, line 3, in backoff_tagger
    backoff = tagger_classes[0](tagged_sents)
File “/usr/local/lib/python2.6/dist-packages/nltk/tag/sequential.py”, line 317, in __init__
    backoff, cutoff, verbose)
File “/usr/local/lib/python2.6/dist-packages/nltk/tag/sequential.py”, line 274, in __init__
    self._train(train, cutoff, verbose)
File “/usr/local/lib/python2.6/dist-packages/nltk/tag/sequential.py”, line 177, in _train
    tokens, tags = zip(*sentence)
ValueError: need more than 1 value to unpack

Is it because some change in NLTK’s API?

Thanks,
Vasilis

Comment on Mnesia Records to MongoDB Documents by Jacob Perkins

Jacob Perkins — Tue, 27 Mar 2012 22:34:00 +0000

The idea with plists is to be a drop-in replacement for functions in lists, with the option of customizing the parallelization by specifying a Malt. ptable then expands on that, but instead of providing a list, you give a mnesia table name and match specification. So for the ptable:foreach functions, F is the function to call on each record in mnesia, Table and Spec are passed thru to mnesia:select in order to select the objects, and Malt is passed to plists:foreach along with F and a list of objects from mnesia. I don’t have an example handy since I haven’t used this code in years, so I recommend reading up on plists & Malt, and mnesia match specifications.

Comment on Mnesia Records to MongoDB Documents by DG

DG — Tue, 27 Mar 2012 20:17:00 +0000

Hi Jacob,
I’m a newbie to erlang (but quite solid in C and DBMS like sqlite, postgres, db2) and trying to use mnesia for a project at university. I found really interesting your comments here and more interesting your elib code… the problem is I’m not able to figure out how to use it
My intentions would be to have parallel processes (read, math functions, write) on data from one or more tables (possibly partitioning on several nodes). I’ve seen you implemented ptable in elib using plist (by you and Stephen Marsh)…

I searched a while for someone using it but seems difficult, surely i’m missing something…
Could you post an (even extremely) simple example of the combination ptable-mnesia?

Comment on Text Classification for Sentiment Analysis – Naive Bayes Classifier by Hiral

Hiral — Fri, 09 Mar 2012 14:19:00 +0000

hi i , i am working on sentiment analysis same positive and negative i want to use
naive bayes but i dnt know technique plz guide me from basic thank you

Comment on Text Classification for Sentiment Analysis – Stopwords and Collocations by Jacob Perkins

Jacob Perkins — Wed, 08 Feb 2012 15:44:00 +0000

words is not explicitly defined above, but it’s a function parameter that is expected to be a list of strings. featx is also a function parameter, but it’s expected to be a function that accepts words and returns a dict. This way, you can pass different featx functions to evaluate_classifier to see the different results.

Comment on Text Classification for Sentiment Analysis – Stopwords and Collocations by Fredrik

Fredrik — Wed, 08 Feb 2012 14:37:00 +0000

I am quite new to Python, and some parts of the code seems more or less magic to me… I have understood that functions are just ordinary objects/values in Python and I guess that this is the trick, but can you explain or suggest a good link for explaining how the following parts of the code work? The name word_feats seems to be bounded to the function word_feats, but what is words bound too? I guess it is bound to featx through function evaluate_classifier, but I really don’t get how featx is assigned a value in
negfeats = [(featx(movie_reviews.words(fileids=[f])), ‘neg’) for f in negids] (to me it looks like featx is a function here, but I guess it is not? I guess that I should do some basic reading about Python, but any clarification would be helpful.

Comment on Text Classification for Sentiment Analysis – Naive Bayes Classifier by » A Text Analysis of Supreme Court Oral Arguments jarv.org

» A Text Analysis of Supreme Court Oral Arguments jarv.org — Sun, 05 Feb 2012 22:47:34 +0000

[...] For more information about sentiment analysis there is some good information here and in these two articles. Applying this to oral arguments? Well let’s leave it as just one way to look at [...]

Comment on Fuzzy String Matching in Python by Jacob Perkins

Jacob Perkins — Sun, 25 Dec 2011 15:55:00 +0000

I’ve never heard of anyone doing that, and I’m not sure how well it would work, because you can’t necessarily break a string down into pieces and match those pieces to match the whole string. For larger strings that might make sense, but the smaller the string, the more exact you should be.

Comment on Fuzzy String Matching in Python by Christopher Stoll

Christopher Stoll — Sun, 25 Dec 2011 05:46:00 +0000

You might be able to replace these techniques (normalization, regex, etc.) with a single dynamic programming algorithm, no? I guess it probably depends upon what you are trying to accomplish though.