Thanks Jacek, I will try to look at these algorithms also.. Thanks for the pointers :)
Regards Stuti -----Original Message----- From: Jacek Wasilewski [mailto:[email protected]] Sent: Wednesday, May 15, 2013 4:52 AM To: [email protected] Subject: Re: Which text classification algo is best for the usecase? Dear Stuti, Thanks for those answers. As far as I know Naive Bayes handles pretty well with text classification - the most common example of Naive Bayes usage is a spam classification. I think you could also try with SVM (Support Vector Machines) and Boosting. Time ago I read some papers where the results of these algorithms in text classification were very good. Unfortunately I haven't had opportunity to implement such a problem using Mahout, so you have to try it youself or maybe some Mahout expert could say a word how to do this. I can only advice that you can check the results of this methods using g.e. Weka or Rapidminer before implementing that with Mahout. I hope I help a little bit and I'm sorry that I couldn't help (yet) with Mahout. Best wishes, Jacek Wasilewski. 2013/5/14 Stuti Awasthi <[email protected]> > Hey Jack, > > Thanks for response. Regarding your queries: > > 1. Classes in which il categorize will range from 3-4 in numbers. Eg > like Problem,Solution,Idea etc 2. The number of keywords or phrase can > vary. It is not fixed in number. > For now Il take around 100 keyword/phrases but later on this will grow. > > Thanks > Stuti Awasthi > > -----Original Message----- > From: Jacek Wasilewski [mailto:[email protected]] > Sent: Tuesday, May 14, 2013 5:23 PM > To: [email protected] > Subject: Re: Which text classification algo is best for the usecase? > > Hi, > > I'm a new here and maybe I'm not an expert in Mahout, but maybe I'll > be able to help you somehow. > > To understand better your problem I have few questions: > 1. Can you provide an example of classes that you'd like to learn? How > many classes are there? > 2. Do you know the total number of this "keywords/phrases" or is it > variant? > > Best wishes, > Jacek Wasilewski. > > > 2013/5/14 Stuti Awasthi <[email protected]> > > > Hi, > > > > I want to perform text classification using Mahout. For now I have > > tried with Naïve Bayes algorithm but I want your suggestion on which > > Algo will be better for my usecase. > > > > Usecase: > > > > I want to classify the text based on custom "keywords/phrases". So > > can I create vectors of the documents in which features are custom > > "keyword/phrases". Basically assume that I have some bag of words > > and phrases based on them I want the classification. > > > > How can we implement such problem in mahout. Is there any already > > existing algorithm which I can use. > > > > Thanks > > Stuti Awasthi > > > > > > > > ::DISCLAIMER:: > > > > -------------------------------------------------------------------- > > -- > > -------------------------------------------------------------------- > > -- > > -------- > > > > The contents of this e-mail and any attachment(s) are confidential > > and intended for the named recipient(s) only. > > E-mail transmission is not guaranteed to be secure or error-free as > > information could be intercepted, corrupted, lost, destroyed, arrive > > late or incomplete, or may contain viruses in transmission. The e > > mail and its contents (with or without referred errors) shall > > therefore not attach any liability on the originator or HCL or its > > affiliates. > > Views or opinions, if any, presented in this email are solely those > > of the author and may not necessarily reflect the views or opinions > > of HCL or its affiliates. Any form of reproduction, dissemination, > > copying, disclosure, modification, distribution and / or publication > > of this message without the prior written consent of authorized > > representative of HCL is strictly prohibited. If you have received > > this email in error please delete it and notify the sender > > immediately. > > Before opening any email and/or attachments, please check them for > > viruses and other defects. > > > > > > -------------------------------------------------------------------- > > -- > > > ---------------------------------------------------------------------- > -------- > > >
