Hello All,
I am currently working on sentimental analysis of social media where in I
am using mahout for vectors generation using bigrams, but while classifying
them under the category some of the unigrams which I dont want are also
coming.
Like
I classified anti English as negative
now in
Hello,
I'm trying to run the hadoop-based recommender job
(org.apache.mahout.cf.taste.hadoop.item.RecommenderJob) from Mahout 0.8 on EMR.
I'm using the Amazon Distribution Hadoop, which is version 1.0.3. Locally
running the job with that version works just fine - I get the expected output.
On
Why do you say that this is unacceptable?
If the phrase is the most common way that the word English is used, this isn't
such a bad thing.
In general, with machine learning, the idea is to let the data speak. If the
data say something you don't like, you have to be careful about
Thank you Sir for your reply.
yes you guessed correct that I am using naive bayes, but how can I handle
this type of problem.
Rather then switching to any other algorithm
With Regards
On Fri, Oct 4, 2013 at 4:21 PM, Ted Dunning ted.dunn...@gmail.com wrote:
Why do you say that this is
Hi Adam,
On Oct 4, 2013, at 4:38am, Adam Warski wrote:
Hello,
I'm trying to run the hadoop-based recommender job
(org.apache.mahout.cf.taste.hadoop.item.RecommenderJob) from Mahout 0.8 on
EMR. I'm using the Amazon Distribution Hadoop, which is version 1.0.3.
Locally running the job
What you are seeing here are the cluster centroids themselves, not the cluster
assignments.
Streaming k-means is a single pass algorithm to derive these centroids.
Typically, the next step is to cluster these centroids using ball k-means.
*Those* results can then be applied back to the
On Fri, Oct 4, 2013 at 6:13 AM, Puneet Arora arorapuneet2...@gmail.comwrote:
yes you guessed correct that I am using naive bayes, but how can I handle
this type of problem.
I didn't hear about a problem.
You said you didn't like weights on words like English to reflect the fact
that they
